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Explain how to choose an appropriate classification algorithm for a particular problem task.
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:
Running random forest algorithm with one variable