3. A Tour of Machine Learning Classifiers Using Scikit-learn

Question 1

Why compare several classifier algorithms?

Answer 1

Because no single classifier works best across all possible scenarios. Consider the number of features or samples, the amount of noise in a dataset, and whether the classes are linearly separable or not. The performance of a classifier, computational power as well as predictive power, depends heavily on the underlying data that are available for learning.

Question 2

What are the 5 main steps in training a machine learning algorithm?

Answer 2

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.