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Machine Learning Interview Questions
Anurag Verma
2 months ago
Machine learning is one of the most
sought-after skills in today's job market, and interviewers are looking for the
best and brightest minds to join their teams. Whether you're a seasoned
professional or just starting out, the interview process can be challenging,
and you need to be prepared to demonstrate your expertise in this cutting-edge
field. To help you get ahead of the competition, we've compiled a list of the
most common machine learning interview questions that you're likely to
encounter during your next interview.
But what sets this
article apart from the others is the focus on not just the questions, but also
the thought process and approach that interviewers are looking for when asking
these questions. With insightful explanations and examples, you'll learn not only
what to expect, but also how to show off your knowledge and skills in a way
that sets you apart from other candidates. So, get ready to take your machine
learning skills to the next level, and ace your next interview with confidence!
1. What do you understand by Machine learning?
Machine learning is a method of data analysis
that automates analytical model building. It is a branch of artificial
intelligence based on the idea that systems can learn from data, identify
patterns and make decisions with minimal human intervention. There are various
types of machine learning such as supervised learning, unsupervised learning,
semi-supervised learning, and reinforcement learning. Machine learning
algorithms can be used for a variety of tasks, such as image recognition,
natural language processing, and making predictions.
Inductive learning is a method of learning by
making generalizations from specific examples. In this method, a model is
trained on a dataset and then used to make predictions about new, unseen data.
It is also known as "bottom-up" learning, as it starts from specific
observations and works its way up to general rules.
Deductive learning, on the other hand, is a
method of learning by applying general rules to specific examples. In this
method, a model is trained on a set of rules or hypotheses and then used to
deduce new information or make predictions. It is also known as
"top-down" learning, as it starts with general rules and applies them
to specific situations.
In summary, Inductive learning is a method of
learning by generalizing from examples while Deductive learning is a method of
learning by applying general rules to examples.
Data mining and machine learning are related
fields, but they are not the same thing.
Data mining is the process of discovering
patterns and knowledge from large data sets. It involves using techniques from
statistics and artificial intelligence to extract insights from data. Data
mining can be used to identify customer segments, detect fraud, or predict
customer behavior.
Machine learning, on the other hand, is a
subfield of artificial intelligence that involves creating algorithms that can
learn from data and make predictions or decisions without being explicitly
programmed. Machine learning algorithms can be used for tasks such as image
recognition, natural language processing, and predictive modeling.
In summary, data mining is focused on
discovering patterns and knowledge from data, while machine learning is focused
on creating algorithms that can learn from data. Data mining is a step in the
process of developing a machine learning model.
Overfitting in machine learning occurs when a
model is trained too well on the training data and performs poorly on new,
unseen data. This happens because the model has learned the noise in the
training data, rather than the underlying pattern that generalizes to new data.
It is a common problem in machine learning and can be addressed by techniques
such as regularization, cross-validation, and early stopping.
Overfitting occurs when a model learns the
detail and noise in the training data to the extent that it negatively impacts
the performance of the model on new data. This can happen when a model is too
complex, such as having too many parameters relative to the amount of training
data. Additionally, overfitting can occur if the model is trained for too many
iterations or on data that is too similar. Regularization techniques can also
be used to reduce overfitting.
There are several methods to avoid
overfitting, including:
- Using more data: The more data you have, the less likely it is that your model will overfit.
- Using fewer features: The fewer features you use, the less likely it is that your model will overfit.
- Regularization: This is a technique used to prevent overfitting by adding a penalty term to the cost function.
- Cross-validation: This is a technique used to evaluate the performance of a model by dividing the data into training and test sets.
- Early stopping: This is a technique used to prevent overfitting by stopping the training process when the performance of the model on a validation set starts to decrease.
- Ensemble methods: This is a technique used to prevent overfitting by combining the predictions of multiple models.
- Dropout Regularization: A popular regularization technique used to reduce overfitting in neural networks by randomly dropping out (setting to zero) some units during the training process.
Supervised machine learning is a type of
machine learning where the model is trained on labeled data, meaning the data
used to train the model includes the correct output or label for each input.
The model learns to make predictions based on the relationship between the
inputs and outputs in the labeled training data. Examples of supervised
learning include regression and classification tasks.
Unsupervised machine learning is a type of
machine learning where the model is not provided with labeled data. Instead,
the model is trained on unlabeled data and must find patterns or relationships
in the data on its own. Examples of unsupervised learning include clustering
and dimensionality reduction tasks.
Machine learning is a broader concept that
encompasses many techniques for training models to make predictions or take
actions based on input data. Deep learning is a specific type of machine
learning that uses neural networks with multiple layers, also known as deep
neural networks, to learn representations of data. While all deep learning
models are machine learning models, not all machine learning models are deep
learning models.
K-Nearest Neighbors (KNN) is a supervised
machine learning algorithm for classification and regression problems, while
k-means is an unsupervised algorithm for clustering problems. KNN finds the k
number of closest examples to a new data point and classifies the point based
on the majority class of its closest neighbors. k-means, on the other hand,
groups similar data points together by identifying k number of centroids in the
data and assigning each point to the nearest centroid. In summary, KNN is used
for classification and regression, k-means for clustering.
There are several types of algorithm methods
in machine learning, including:
- Supervised learning: algorithms that learn from labeled training data
- Unsupervised learning: algorithms that learn from unlabeled data
- Semi-supervised learning: algorithms that combine elements of both supervised and unsupervised learning
- Reinforcement learning: algorithms that learn from interactions with an environment
- Deep learning: algorithms that use neural networks with multiple layers to learn from data
Within these categories, there are many
specific algorithm methods, such as linear regression, k-means, and Random
Forest for supervised, unsupervised, and deep learning respectively.
Reinforcement Learning (RL) is a type of
machine learning in which an agent learns to make decisions by interacting with
its environment in order to maximize a reward signal. The agent continuously
takes actions in an environment, and the environment provides feedback in the
form of rewards or penalties. The agent's goal is to learn a policy that
maximizes the expected cumulative reward over time. RL is used in a wide range
of applications, including robotics, game playing, and decision making.
The trade-off between bias and variance refers
to the relationship between the complexity of a model and its ability to fit
the training data well while also generalizing well to new, unseen data. A
model with high bias is one that makes strong assumptions about the form of the
relationship between the input and output variables, which can lead to a
simpler model that is less likely to overfit the training data, but also may
not capture the true relationship. A model with high variance is one that is
very flexible, which can lead to a model that fits the training data very well,
but is likely to overfit and perform poorly on new, unseen data. The trade-off
between bias and variance is often addressed by techniques such as
regularization, which aim to balance the complexity of the model with its
ability to generalize well.
Classification and regression are both types
of supervised learning in machine learning, but they are used for different
types of problems.
Classification is used for predicting a
categorical label, such as "spam" or "not spam" for an
email, or "cancer" or "no cancer" for a medical image. The
goal of classification is to accurately assign a predefined set of labels to
input data.
Regression, on the other hand, is used for
predicting a continuous value, such as the price of a stock or the temperature
tomorrow. The goal of regression is to find the best fit line or curve that
represents the relationship between the input data and the continuous output
value.
In short, classification is used for
predicting discrete categories, while regression is used for predicting
continuous values.
The five popular algorithms in machine
learning are:
- Linear Regression
- Logistic Regression
- Decision Trees
- Random Forest
- Gradient Boosting (GBM)
Ensemble learning is a method of training
multiple models and combining their predictions to achieve better performance
than any single model alone. This can be done by using a variety of techniques,
such as averaging the predictions of multiple models, or training a meta-model
to make the final prediction based on the predictions of the individual models.
The goal of ensemble learning is to reduce the variance and bias of the overall
model by combining the strengths of multiple models.
Model selection in machine learning is the
process of choosing the best model from a set of candidate models for a given
dataset and task. This process typically involves evaluating the performance of
each candidate model using a specific metric, such as accuracy or AUC, and
selecting the model that performs the best. Model selection can also involve
tuning the hyperparameters of each candidate model to improve its performance.
This process can be automated using techniques such as cross-validation, grid
search, or Bayesian optimization.
The three stages of building a machine
learning model are:
Data preparation and feature engineering, in
which the raw data is cleaned and transformed into a format that can be used to
train the model.
Model training, in which the prepared data is
used to train the model using a specific algorithm.
Model evaluation, in which the trained model
is tested on a separate dataset to evaluate its performance and make any
necessary adjustments.
The standard approach to supervised learning
typically involves the following steps:
- Collect and clean the training data.
- Choose a model architecture and train the model on the training data.
- Evaluate the model on a hold-out validation set.
- Fine-tune the model's hyperparameters and repeat step 3 until the model performs well on the validation set.
- Test the model on unseen test data to estimate its performance on new data.
Note that this approach is not always the best
one, there are various other approaches like Semi-supervised learning,
unsupervised learning, online learning, etc.
A training set is a set of data used to train
a machine learning model. It is used to teach the model to recognize patterns
and relationships in the data, so that it can make accurate predictions or
decisions when presented with new data.
A training test is a subset of the training
set that is used to evaluate the performance of the model during the training
process. It is used to determine how well the model is able to learn from the
training data, and to identify any problems or issues that need to be addressed
before the model is deployed. The model's performance on the training test is
used to adjust the model's parameters and improve its accuracy.
There are several ways to handle missing data
in a dataset, including:
- Dropping rows or columns with missing data: This is simple and efficient but can lead to loss of information if the amount of missing data is large.
- Imputing missing values: This involves replacing missing values with statistical estimates, such as the mean or median of the non-missing values.
- Using multiple imputations: This involves generating multiple imputed datasets, and then combining the results.
- Using prediction models: This involves training a model to predict missing values based on the observed data.
- Using data augmentation methods like back-fill and forward fill method
The choice of method will depend on the amount
of missing data, the nature of the data, and the research question. It's often
a good idea to try multiple methods and compare their results.
ILP stands for Integer Linear Programming,
which is a method to find the optimal solution of a mathematical model that
consists of linear relationships between variables, subject to constraints that
the variables must be integers. It is a type of mathematical optimization
problem that is commonly used in operations research, management science, and
computer science to find the best solution in situations where some or all of
the variables are required to be integers.
The steps involved in a machine learning
project typically include:
- Defining the problem and determining the goals of the project.
- Collecting and preprocessing the data, including cleaning and formatting the data, handling missing or incomplete data, and possibly scaling or normalizing the data.
- Selecting and training a model, which may involve selecting features, choosing an algorithm, and tuning hyperparameters.
- Evaluating the model, including measuring its performance using metrics such as accuracy or F1 score, and possibly using techniques such as cross-validation to ensure that the results are robust.
- Deploying the model in a production environment and monitoring its performance over time.
- Continuously improve the model by retraining with new data and updating the model based on feedback from the production environment.
Precision and recall are two measures of a
classifier's performance.
Precision is a measure of the accuracy of
positive predictions. It is the number of true positive predictions divided by
the number of true positive plus false positive predictions. A high precision
means that there are few false positives.
Recall is a measure of the classifier's
ability to find all positive instances. It is the number of true positive
predictions divided by the number of true positive plus false negative
predictions. A high recall means that there are few false negatives.
In general, increasing precision reduces
recall and vice versa. A perfect classifier would have a precision of 1 and
recall of 1, but in practice it's a trade-off between the two.
A decision tree is a type of machine learning
algorithm used for both classification and regression problems. It is a
tree-like model of decisions and their possible consequences, represented
graphically. The topmost node in a decision tree is known as the root node. It
splits the data into subsets, and each internal node in the tree corresponds to
a test on an attribute, each branch represents the outcome of the test, and
each leaf node represents a class label. The goal is to create a model that
predicts the value of a target variable by learning simple decision rules inferred
from the data features. The decision tree algorithm repeatedly partitions the
data into subsets based on the values of the input features until the leaf
nodes, which contain the predictions.
Supervised learning is a type of machine
learning where a model is trained on a labeled dataset, where the correct
output for each input is provided. The model is then able to make predictions
on new, unseen data. The main functions of supervised learning are:
Classification: The model is trained to assign
input data to one or more predefined categories or classes.
Regression: The model is trained to predict a
continuous value for a given input.
Time series forecasting: The model is trained
to predict future values in a time series based on past values.
Anomaly detection: The model is trained to
identify patterns or observations that do not conform to expected behavior.
Unsupervised learning is a type of machine
learning where the model is not provided with labeled data. Instead, the model
is given a dataset and must find patterns or relationships within the data on
its own. Some common functions of unsupervised learning include:
Clustering: grouping similar data points
together based on their features.
Dimensionality reduction: reducing the number
of features in a dataset while preserving important information.
Anomaly detection: identifying data points
that are different from the norm.
Generative modeling: creating new data that is
similar to the input data.
Association rule learning: discovering
relationships between variables in a dataset.
Algorithm independent machine learning refers
to the idea of developing machine learning models that are not tied to a
specific algorithm or set of algorithms. This allows the model to be more
flexible and adaptable to different types of data or problem domains, without
being constrained by the assumptions or limitations of a particular algorithm.
This can be achieved by using ensemble methods, meta-learning, or other
techniques that allow the model to learn and adapt to different inputs or
conditions. Algorithm independent machine learning is a field of research that
is still in its early stages and there is a lot of ongoing research in this
area.
A classifier in machine learning is a model
that assigns input data to one or more predefined categories or classes. The
classifier is trained on a labeled dataset, where each input is associated with
a specific class label. The classifier uses the patterns and relationships
learned from the training data to make predictions on new, unseen data. Common
types of classifiers include decision trees, k-nearest neighbors, and support
vector machines.
Genetic programming (GP) is a method of
evolving computer programs or systems that imitates the process of natural
evolution. It is a subset of machine learning and artificial intelligence that
uses principles of genetics and natural selection to generate and improve computer
programs. GP starts with a population of initial solutions (often in the form
of computer programs) and applies genetic operators such as mutation and
crossover to generate new and improved solutions over multiple generations. The
goal is to evolve a population of solutions that optimally solve a given
problem.
Support Vector Machine (SVM) is a supervised
learning algorithm that can be used for classification and regression tasks. In
classification, SVM aims to find the best hyperplane (decision boundary) that
separates the data into different classes. SVM can handle linear and non-linear
classification problems. For linear problems, SVM finds the hyperplane that
maximizes the margin, which is the distance between the hyperplane and the
closest data points from each class. For non-linear problems, SVM uses a technique
called the kernel trick to transform the data into a higher dimensional space
where a linear hyperplane can be used for separation.
SVM can handle binary and multi-class
classification problems. In a binary classification problem, SVM finds a single
hyperplane to separate the two classes. In a multi-class classification
problem, SVM uses one-vs-one or one-vs-all strategy to find multiple
hyperplanes to separate the classes.
A linked list is a data structure that
consists of a sequence of elements, each of which contains a reference (or
"link") to the next element in the sequence. The elements are not
stored in contiguous memory locations, as they are in an array, but are
scattered throughout memory and linked together via the references. This allows
for efficient insertion and deletion operations, but makes accessing elements
by index less efficient.
An array is a data structure that stores a
fixed-size sequence of elements of the same type, in contiguous memory
locations. Elements can be accessed by their index, which is an integer that
represents the position of the element in the array. This allows for efficient
access, but makes inserting and deleting elements less efficient, since all
elements after the insertion/deletion point need to be moved.
A confusion matrix is a table that is often
used to describe the performance of a classification algorithm. Each row of the
matrix represents the instances in a predicted class while each column
represents the instances in an actual class (or vice versa). The name
"confusion matrix" is derived from the fact that it makes it easy to
see if the system is confusing two classes (i.e. commonly mislabeling one as
another). It is a way of summarizing the performance of a classification
algorithm, and allows you to compute various metrics such as accuracy,
precision, recall, and F1 score.
A confusion matrix is a table that is used to
define the performance of a classification algorithm. It is used to describe
the performance of a classification model on a set of test data for which the
true values are known. The elements of the matrix are the number of true
positives (TP), false positives (FP), true negatives (TN), and false negatives
(FN).
True Positive (TP) is the number of correct
positive predictions. For example, in a binary classification problem to
predict whether a person has cancer or not, a true positive would be a case
where the model correctly predicts that the person has cancer.
True Negative (TN) is the number of correct
negative predictions. For example, in a binary classification problem to
predict whether a person has cancer or not, a true negative would be a case
where the model correctly predicts that the person does not have cancer.
False Positive (FP) is the number of incorrect
positive predictions. For example, in a binary classification problem to
predict whether a person has cancer or not, a false positive would be a case
where the model incorrectly predicts that the person has cancer, but in
reality, the person does not.
False Negative (FN) is the number of incorrect
negative predictions. For example, in a binary classification problem to
predict whether a person has cancer or not, a false negative would be a case
where the model incorrectly predicts that the person does not have cancer, but
in reality, the person has cancer.
An example of a confusion matrix:
Actual
Predicted
Positive Negative
Positive
TP FP
Negative
FN TN
In this example, the confusion matrix would
have TP = true positives, TN = true negatives, FP = false positives and FN =
false negatives.
Both model accuracy and model performance are
important considerations in machine learning, but their relative importance can
depend on the specific use case.
Model accuracy refers to how well a model
correctly classifies or predicts the target variable. It is typically measured
using metrics such as accuracy, precision, recall, and F1 score.
Model performance, on the other hand, refers
to how well a model runs in terms of speed and resource usage. It is typically
measured using metrics such as inference time, memory usage, and power
consumption.
In some cases, such as in real-time systems or
mobile applications, model performance is more important than accuracy because
the model needs to run quickly and efficiently. In other cases, such as in
medical diagnosis, accuracy is more important than performance because a
incorrect decision could have severe consequences.
So it will depends on the specific use case,
the trade-off between model accuracy and performance must be carefully
considered.
Bagging and Boosting are two ensemble methods
used to improve the performance of machine learning models.
Bagging stands for Bootstrap Aggregating. It
is a technique where multiple models are trained on different subsets of the
training data, which are created by randomly sampling the original data with
replacement. The final output is the average or majority vote of the individual
models. This reduces overfitting by averaging out the errors made by each
model.
Boosting is an ensemble method that attempts
to combine a set of weak learners to create a strong learner. It works by
training a weak model, and then training another weak model to correct the
errors made by the first one. This process is repeated multiple times, with
each subsequent model focusing on the mistakes made by the previous models. The
final output is the weighted sum of the individual models.
Both bagging and boosting are used to improve
the performance of machine learning models by reducing overfitting and
increasing generalization.
Bagging and boosting are both ensemble methods
used to improve the performance of machine learning models.
Similarities:
- Both methods use multiple models to improve the overall performance of the system.
- Both methods can be applied to a variety of models, including decision trees and neural networks.
Differences:
- Bagging (short for Bootstrap Aggregating) creates multiple independent models by training on different subsets of the data. These subsets are created by randomly sampling the data with replacement. Bagging reduces the variance of the models by averaging the predictions of multiple models.
- Boosting, on the other hand, trains multiple models in sequence, where each model tries to correct the errors made by the previous model. The final prediction is made by combining the predictions of all the models. Boosting reduces the bias of the models by giving more weight to the examples that are hard to classify.
- Bagging is known to improve the stability and accuracy of the model while Boosting is known to improve the accuracy of the model by reducing bias.
- Bagging is a parallel ensemble method as all the models are trained independently, Boosting is a sequential ensemble method as it trains the model in sequence.
Cluster sampling is a sampling technique in
which clusters of units are selected from a larger population, and all units
within the chosen clusters are included in the sample. In other words, instead
of selecting individual units from a population at random, as in simple random
sampling, in cluster sampling, groups of units are selected at random. The
units within each cluster are then studied to make inferences about the
population as a whole. This method is useful when the population is dispersed
over a wide area or when it is difficult or expensive to obtain a complete list
of the units in the population
A Bayesian network is a probabilistic
graphical model that represents a set of variables and their probabilistic
dependencies using a directed acyclic graph (DAG). Each node in the graph
represents a variable, and the edges between nodes represent the probabilistic
dependencies between the variables. The probability of a variable is determined
by the values of its parent nodes in the graph, and the network can be used to
make probabilistic inferences about the variables given some observed data.
Bayesian networks are particularly useful for modeling systems with a large
number of variables and complex dependencies between them, and they have been
applied in a wide range of fields, including artificial intelligence,
bioinformatics, and finance.
The two components of a Bayesian logic program
are a set of logical rules, and a set of probabilistic statements (or
distributions) associated with those rules. The rules are used to infer new
information, while the probabilistic statements are used to represent
uncertainty about the truth of certain statements. Together, these two
components allow for reasoning under uncertainty using a combination of logical
and probabilistic methods.
Dimension reduction is a technique used in
machine learning to reduce the number of features (or dimensions) in a dataset
while retaining as much information as possible. This can be useful in cases
where the dataset has a large number of features, as it can lead to
overfitting, increased computation time, and difficulty in interpreting the
model. Common dimension reduction techniques include principal component
analysis (PCA), linear discriminant analysis (LDA), and t-distributed
stochastic neighbor embedding (t-SNE). These techniques transform the original
features into a new set of features with fewer dimensions, which can then be
used in a machine learning model.
Instance-based learning algorithms are
sometimes referred to as "lazy" learning algorithms because they do
not build a model until a prediction is requested. Instead, they store the
training instances in memory and use them to make predictions when needed.
Because the model is not built until it is needed, these algorithms are
considered "lazy" in comparison to algorithms that build a model as
soon as the training data is available.
The F1 score is a measure of a test's
accuracy. It considers both the precision and the recall of the test to compute
the score. The F1 score is the harmonic mean of the precision and recall, where
an F1 score reaches its best value at 1 (perfect precision and recall) and
worst at 0.
A decision tree can be pruned by removing
branches that do not provide much information gain, or by setting a threshold
for the maximum depth of the tree. This can help to prevent overfitting and
improve the generalization performance of the model. One common method for
pruning decision trees is reduced error pruning, where a branch is removed if
the accuracy of the tree is not significantly decreased after the branch is
removed. Another method is cost complexity pruning, where a complexity
parameter is introduced to balance the trade-off between the accuracy of the
tree and the number of its leaves.
There are many recommended systems in machine
learning, depending on the task and the type of data. Some popular systems
include:
- Random Forest for classification and regression tasks
- Gradient Boosting (GBM) for classification and regression tasks
- Support Vector Machines (SVMs) for classification tasks
- k-Nearest Neighbors (k-NN) for classification and regression tasks
- Neural networks, such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks for image and time series data respectively
- Transfer Learning
- XGBoost
It's also worth noting that it's often a good
idea to try multiple different models and compare their performance on your
specific task and dataset.
Underfitting occurs when a machine learning
model is not able to capture the underlying pattern of the data. This results
in a model that performs poorly on both the training data and new, unseen data.
This is often the result of a model that is too simple or has too few
parameters relative to the complexity of the data.
Regularization becomes necessary in machine
learning when a model is overfitting the training data. Overfitting occurs when
a model is too complex and is able to memorize the training data, but is not
able to generalize well to new, unseen data. Regularization methods, such as L1
and L2 regularization, add a penalty term to the model's loss function to
discourage large weights, which can help to prevent overfitting and improve the
model's generalization performance. Additionally, it is also used when the data
is high-dimensional.
Regularization is a technique used in machine
learning to prevent overfitting. Overfitting occurs when a model is too complex
and learns the noise in the training data rather than the underlying pattern.
Regularization adds a penalty term to the loss function that the model is
trying to minimize. This penalty term discourages the model from assigning too
much weight to any one feature, which helps to reduce overfitting. There are
several types of regularization, including L1, L2, and dropout. L1 and L2
regularization add a penalty term to the loss function that is proportional to
the absolute or square value of the weight, respectively. Dropout is a form of
regularization that randomly drops out (i.e., sets to zero) some of the neurons
in the network during training, which helps to prevent complex co-adaptations
of neurons.
Categorical variables are variables that can
take on one of a limited set of values. In R, categorical variables are stored
as character vectors or integers. However, many modeling techniques, such as
linear and logistic regression, require that the input variables be numeric.
Therefore, it is necessary to convert categorical variables into factors before
using them in these types of models.
The two main functions in R used to perform
this conversion are as.factor() and factor(). as.factor() is used to convert a
character or numeric vector into a factor, while factor() is used to create a
new factor variable. Both functions take one or more arguments specifying the levels
(i.e. possible values) of the factor variable, and an optional argument
specifying the level labels.
For example, if you have a variable x that is
a character vector containing values "a", "b", and
"c", you can convert it to a factor variable with levels
"a", "b", and "c" using the following code:
x <- c("a", "b",
"c")
x_factor <- as.factor(x)
Or
x_factor <- factor(x)
Both of the above code will give you the same
output where x_factor will be a factor variable with levels "a",
"b", "c".
It depends on the specific data and the model
being used. In general, using a categorical variable as a continuous variable
could lead to a better predictive model if the categorical variable has a clear
ordinal relationship or if the model is better suited to continuous variables.
However, using a categorical variable as a continuous variable could also lead
to poor performance if the categorical variable does not have a clear ordinal
relationship and the model is not well suited to continuous variables. It is
always important to carefully evaluate the assumptions and the appropriateness
of the data types used in a model.
Machine learning is used in a variety of ways
in day-to-day life, including:
- Recommender systems, which suggest products or content to users based on their past behavior
- Image and speech recognition, which are used in personal assistants and mobile device features
- Fraud detection in financial transactions
- Email spam filtering
- Self-driving cars
- Personalized medicine
- Predictive maintenance in manufacturing and other industries
- Natural language processing in virtual assistants, chatbots, and language translation tools.
Machine Learning Interview Questions For Freshers
Machine
learning was introduced as a way to allow computers to learn from data, without
being explicitly programmed. It automates the process of finding patterns in
data and making predictions or decisions based on those patterns. The goal of
machine learning is to develop algorithms that can learn from experience and
improve their performance over time.
Supervised
Learning: Regression, Decision Trees, Random Forest, SVM, Naive Bayes, KNN
Unsupervised
Learning: K-Means, Hierarchical Clustering, PCA, Autoencoders
Semi-Supervised
Learning
Reinforcement
Learning
Transfer
Learning.
Supervised
learning is a type of machine learning where the algorithm is trained on a
labeled dataset, where the correct output is already known, to make predictions
or classify new examples.
Unsupervised learning is a type of machine
learning where the model is trained on unlabeled data and the goal is to find
patterns or relationships in the data without any prior knowledge or labels.
The term "naive" in Naive Bayes
refers to the assumption of independence between each feature in the input
data. This assumption simplifies the calculations required to make a prediction
and often leads to good performance in practice despite being a strong and
unrealistic assumption.
PCA
(Principal Component Analysis) is a dimensionality reduction technique that
aims to simplify a high-dimensional dataset by transforming it into a set of
linearly uncorrelated variables called principal components, where the first
principal component retains the maximum variance and each successive component
has the highest variance possible under the constraint that it is orthogonal to
the previous components.
PCA
is used when:
- Visualizing high-dimensional data
- Data compression and reducing storage requirements
- Improving machine learning algorithms' performance by removing correlated features or reducing noise.
Support
Vector Machine (SVM) is a supervised learning algorithm that can be used for
classification or regression tasks. It is based on the idea of finding the
hyperplane that best separates the data into different classes, so that the
data points closest to the hyperplane (called support vectors) have the
greatest impact on the decision boundary.
SVM
algorithms work by mapping the data into a high-dimensional feature space and
finding the hyperplane with the maximum margin, which separates the classes
with the largest distance. The maximum margin classifier is guaranteed to have
the best generalization performance compared to other hyperplanes.
In
SVM, the optimization problem is solved using the Lagrange multipliers method,
where the margin and classification constraints are formulated as a quadratic
optimization problem. The solution is then obtained using a number of
optimization algorithms, such as gradient descent, coordinate descent, and
Newton's method.
Additionally,
SVM has a regularization parameter, "C", that allows the trade-off
between a good margin and a correct classification of the training data. A
large value of C indicates a low tolerance for misclassified samples, while a
small value of C means a high tolerance.
In
practice, SVM can also handle non-linearly separable data using kernel
functions, which map the data into a higher-dimensional space where a linear
hyperplane can be found. Commonly used kernel functions include polynomial,
radial basis function (RBF), and sigmoid.
In
summary, SVM is a powerful and versatile algorithm that can be applied to a
wide range of problems in machine learning, including classification,
regression, and anomaly detection.
Support
vectors in SVM are the training samples that are closest to the decision
boundary and determine its position. They have the greatest impact on the
classifier's margins and help determine the best boundary between classes.
SVM (Support Vector Machine) has several types
of Kernels which are used to transform the input data into a higher dimensional
space. Some of the most common Kernels are:
- Linear Kernel
- Polynomial Kernel
- Radial basis function (RBF) Kernel
- Sigmoid Kernel
- Laplacian Kernel
- Bessel Function Kernel
- ANOVA radial basis function (ARBF) Kernel.
Cross-Validation
is a technique in machine learning to evaluate the performance of a model on
unseen data. It involves dividing a dataset into multiple partitions and
training the model on one partition while evaluating its performance on the
other partition(s). The process is repeated multiple times to average out the
performance of the model.
Bias
in machine learning refers to the systematic error in a model's predictions
that result in unequal treatment of different groups. It occurs when the
training data contains a skewed representation of the population, causing the
model to make incorrect assumptions and perpetuating these biases in its
predictions. This can result in discriminatory outcomes and undermine the
fairness of the model's decisions.
Classification
and Regression are two types of supervised learning problems in machine
learning.
Classification
is a problem of categorizing data into predefined classes based on a set of
features. The goal is to predict the class label of new instances based on
previous training data.
Regression,
on the other hand, is a problem of predicting a continuous value for a given
input. The goal is to fit a mathematical model to the input-output
relationship, so that the model can be used to predict the output for new
inputs.
In
summary:
Classification:
Predict class label (Discrete output)
Regression:
Predict continuous value (Continuous output)
F1 Score is a measure of a model's accuracy,
calculated as the harmonic mean of precision and recall. It is commonly used in
binary classification problems, where the goal is to identify a positive class
(e.g. spam or not spam).
In using F1 Score, one would calculate
precision and recall for a model and then use the formula:
F1 = 2 * (precision * recall) / (precision +
recall)
To use the F1 Score, you would pick a
threshold for classifying a sample as positive (e.g. probability > 0.5) and
then evaluate the model's performance in terms of precision, recall and F1. A
high F1 score indicates a balance between high precision and high recall,
meaning that the model makes few false positive and false negative predictions.
A
neural network is a type of machine learning model inspired by the structure
and function of the human brain, composed of interconnected processing nodes
called artificial neurons. It can learn to perform tasks by analyzing training
data and making predictions or decisions based on that analysis.
Loss Function and Cost Function are both
mathematical measures used to evaluate the performance of a machine learning
model.
Loss Function, also known as objective
function, measures the difference between the predicted output and the actual
output of a model. The aim of a loss function is to minimize the difference
between the two values so that the model can predict the output accurately.
Some common examples of loss functions include Mean Squared Error,
Cross-Entropy, and Hinge Loss.
Cost Function, on the other hand, is the sum
of loss functions for all the training data samples. It represents the total
cost of the model's predictions for the entire training data set. The objective
of a cost function is to minimize its value, which is achieved by minimizing
the loss function.
The key difference between the two is that
Loss Function focuses on the prediction error for a single data sample, while
Cost Function aggregates the prediction error for the entire data set. In
simpler terms, Loss Function is a single value that represents the prediction
error for a single sample, while Cost Function is the sum of all Loss Functions
for all the samples.
In conclusion, Loss Function is used to
evaluate the performance of a model for a single data sample, while Cost
Function is used to evaluate the performance of a model for the entire data
set.
To determine which machine learning algorithm
to use, consider the following factors:
- Problem type: Supervised, Unsupervised, Reinforcement, etc.
- Size and quality of data: Small data, imbalanced data, high-dimensional data, etc.
- Performance requirements: Execution time, accuracy, interpretability, etc.
- Domain knowledge: Prior knowledge about the problem and available resources.
- Model interpretability: How well the model's decision-making process can be understood and explained.
Once you have evaluated these factors, you can
shortlist a few algorithms and compare their performance through
experimentation and cross-validation.
As a language model AI, I can provide you some
steps to handle outlier values:
- Determine the source of the outliers: Before handling outliers, it is important to determine why they exist in the first place. This may help you decide whether to include or exclude the outliers from your analysis.
- Visualize the data: Visualization can help you identify outliers and patterns in the data. This can help you determine whether the outliers are legitimate values or errors in the data.
- Use statistical methods: Statistical methods such as z-scores and the interquartile range (IQR) can help you identify outliers in a dataset. Z-scores are a measure of how many standard deviations a value is from the mean, while the IQR measures the spread of the middle 50% of the data.
- Remove outliers: Depending on the source of the outliers, you may want to remove them from your analysis. This can help you avoid the effects of outliers on your results.
- Transform the data: If outliers are a result of skewed data, you may want to transform the data to make it more normal. This can include transforming the data using a logarithmic or square root transformation.
- Use robust statistics: If you are concerned about outliers affecting your results, you may want to use robust statistics that are less sensitive to outliers.
It is important to remember that outliers are
a natural part of data, and the way you handle them will depend on the nature
of your analysis and the data you are working with.
A Random Forest is an ensemble learning method
for classification and regression problems in machine learning. It is a
collection of decision trees, where each tree is trained on a random subset of
the data and the outputs of all trees are combined to produce the final output.
The method works as follows:
- Bootstrapping: The training data is randomly sampled with replacement to create multiple sets of training data, also known as bootstrapped samples.
- Tree Generation: For each bootstrapped sample, a decision tree is trained and grows by repeatedly splitting the data on the feature that provides the largest information gain.
- Tree Prediction: Each tree produces a prediction for a given input data point.
- Combining Predictions: The predictions from all trees are combined into a single prediction by taking the majority vote for classification problems, or by taking the average for regression problems.
The main advantage of a Random Forest is that
it reduces the overfitting problem that occurs in decision trees by combining
the predictions of multiple trees. Additionally, it also provides a measure of
feature importance, which can be used to identify the most important features
in the data.
Collaborative Filtering: A technique in
recommender systems that utilizes the past behavior of users to recommend items
to them. It is based on the idea that people who have similar preferences in
the past will have similar preferences in the future.
Content-Based Filtering: A technique in
recommender systems that utilizes the attributes or features of items to
recommend similar items to users. It is based on the idea that if a user likes
a certain item, they are likely to like items with similar attributes.
Clustering is an unsupervised learning
technique in machine learning that partitions data into groups (clusters) based
on their similarity. The goal is to separate data points into clusters so that
data points in the same cluster are more similar to each other than those in
different clusters.
9. How can you select K for K-means Clustering?
There are several methods to select the value
of K for K-means clustering, including:
Elbow Method: The elbow method involves
plotting the within-cluster sum of squared distances (WCSS) against the number
of clusters (K). The optimal value of K is the point where the WCSS begins to
level off, also known as the "elbow."
Silhouette Method: The silhouette method
measures the similarity of each point within a cluster. The silhouette score
ranges from -1 to 1, with a score close to 1 indicating a well-defined cluster
and a score close to -1 indicating poor clustering. The optimal value of K is
the number of clusters with the highest average silhouette score.
Gap Statistic: The gap statistic measures the
difference between the WCSS of the observed data and a null reference dataset.
The optimal value of K is the number of clusters where the gap statistic starts
to level off.
Domain Expertise: If the data being analyzed
has a known structure, domain expertise can be used to select the appropriate
number of clusters.
Ultimately, the selection of K is not a
precise science and may require some experimentation to determine the optimal
value.
Recommender
Systems are a type of artificial intelligence (AI) system that analyze user
behavior and preferences to make personalized recommendations. These systems
use algorithms to predict what items or services a user is most likely to be
interested in, based on their past interactions, behavior, and preferences.
They are commonly used in e-commerce, entertainment, and social media platforms
to suggest products, movies, music, books, etc. to users. The goal of
recommender systems is to enhance the user experience by providing relevant and
customized recommendations, and to increase customer engagement and sales.
11. How do check the Normality of a dataset?
As a
language model AI, I can suggest several ways to check the normality of a
dataset:
Visual
inspection: Plotting a histogram, Q-Q plot, or normal probability plot of the
dataset can help to visually determine if the data is approximately normal.
Skewness
and Kurtosis: These are two statistical measures that describe the shape of the
distribution. Skewness measures the symmetry of the data, while kurtosis
measures the peakedness of the data. Normal data should have a skewness of 0
and kurtosis of 3.
Shapiro-Wilk
test: This is a statistical test that compares the sample data to a normal distribution.
The test returns a p-value, which represents the probability that the sample
came from a normal distribution. A p-value greater than 0.05 indicates that the
data is likely normal.
Anderson-Darling
test: This is another statistical test that checks for normality. It is similar
to the Shapiro-Wilk test, but it is more sensitive to deviations from
normality.
D’Agostino’s
K^2 test: This test checks for normality by transforming the data and then
testing for normality. The test returns a p-value, which indicates the
probability that the data came from a normal distribution.
Note:
No single method can prove that a dataset is normal, but several methods can be
used in combination to increase confidence in the normality of the data.
Yes,
logistic regression can be used for more than two classes. This is known as
multinomial logistic regression. In multinomial logistic regression, the
response variable is categorical with more than two possible outcomes, and the
goal is to model the relationship between the independent variables and the
probabilities of each outcome
Correlation refers to the relationship between
two variables and how they change together. It is a statistical measure that
indicates the strength and direction of a linear relationship between two
variables. Correlation ranges from -1 to 1, with -1 indicating a strong
negative correlation, 1 indicating a strong positive correlation, and 0 indicating
no correlation.
Covariance is a measure of the degree to which
two variables change together. It is calculated as the product of the
deviations of each variable from their mean, divided by the number of
observations. Covariance values can be positive or negative, indicating a
positive or negative relationship between the variables, respectively. However,
covariance does not indicate the strength of the relationship, which is why
correlation is often preferred over covariance in analyzing relationships
between variables.
P-value
is a statistical measure used to determine the significance of a hypothesis
test. It is the probability of observing a test statistic as extreme or more
extreme than the one computed from the sample, assuming the null hypothesis is
true. A low P-value (typically < 0.05) indicates strong evidence against the
null hypothesis, while a high P-value suggests weak evidence against the null
hypothesis.
Parametric
models are mathematical models that describe the relationships between
variables using a limited set of parameters. The parameters of the model are
estimated using statistical methods, such as maximum likelihood estimation or
least squares regression. These models are based on a set of assumptions about
the distribution of the data, and the number of parameters is usually limited
to a few. Examples of parametric models include linear regression, logistic
regression, and polynomial regression.
Non-parametric
models, on the other hand, do not make any assumptions about the distribution
of the data. Instead, they use a more flexible approach to describe the
relationships between variables, relying on a large number of data points to
estimate the underlying relationships. Non-parametric models do not have any
fixed parameters, and the number of parameters grows with the number of data
points. Examples of non-parametric models include decision trees, random
forests, and k-nearest neighbors.
In
general, parametric models are easier to interpret and can be more efficient,
but they can be limited in their ability to capture complex relationships in
the data. Non-parametric models are more flexible, but can be more difficult to
interpret and may be computationally more intensive. The choice between
parametric and non-parametric models often depends on the nature of the data
and the research question being addressed.
Sigmoid
and Softmax functions are two different activation functions used in machine
learning and deep learning.
Sigmoid Function:
- The Sigmoid function maps any input value to the range of 0 to 1.
- It is used for binary classification problems where the output can only be one of two classes (0 or 1).
- The Sigmoid function is used to predict the probability of a binary event occurring.
- The Sigmoid function is a good choice when the output is a binary classification because it helps to prevent overfitting and make the model more robust.
Softmax Function:
- The Softmax function maps any input values to the range of 0 to 1.
- It is used for multiclass classification problems where the output can be one of several classes.
- The Softmax function is used to predict the probability of each class.
- The Softmax function is a good choice when the output is a multiclass classification because it helps to prevent overfitting and makes the model more robust.
In
conclusion, the main difference between the Sigmoid and Softmax functions is
that the Sigmoid function is used for binary classification and the Softmax
function is used for multiclass classification.
17. What is Epoch in Machine Learning?
An
epoch in machine learning is a complete iteration through all the samples in a
dataset during the training process of a model. The model's parameters are
updated after each epoch, allowing it to gradually improve its performance on
the training data.
18. What is Bayes’s Theorem in Machine Learning?
Bayes'
Theorem is a mathematical formula used to calculate the probability of an event
based on prior knowledge of conditions that might be related to the event. In
machine learning, Bayes' theorem is used to calculate the probability of a
class label given the features in a dataset. This can be used to make
predictions about the class labels of new data.
19. What is Hypothesis in Machine Learning?
Hypothesis
in Machine Learning is an assumption or prediction about the relationship between
the input and output variables in a dataset. It is a statement that describes
how a model is expected to behave based on the input data. In simple terms, it
is a tentative explanation for the relationship between variables that can be
tested through experiments or observations. The purpose of a hypothesis is to
guide the development of a model and make predictions about future data based
on the relationship between the input and output variables.
Conclusion
In conclusion, machine learning interview questions are becoming increasingly important for employers to ask as the demand for this type of technology grows. It is important for employers to understand the basics of machine learning and the types of questions to ask in order to ensure they are hiring the best candidate for the job. Additionally, it is important for job seekers to understand the types of questions they may be asked in order to be prepared and demonstrate their knowledge. With the right preparation, employers and job seekers can ensure that the machine learning interview process is successful.