How to Choose a classification algorithm for particular problem?

Choosing an appropriate classification algorithm for a particular problem task requires practice: each algorithm has its own features and is based on certain assumptions.

In practice, it is always recommended that you compare the performance of at least a handful of different learning algorithms to select the best model for the particular problem. These may differ in the number of features or samples, the amount of noise in a dataset, and whether the classes are linearly separable or not.

Eventually, the performance of a classifier, computational power as well as predictive power, depends heavily on the underlying data that are available for learning.

The five main steps that are involved in training a machine learning algorithm can be summarized as follows:

1. Selection of features.
2. Choosing a performance metric.
3. Choosing a classifier and optimization algorithm.
4. Evaluating the performance of the model.
5. Tuning the algorithm.