Feature Engineering Made Easy

Evaluating supervised learning algorithms

When performing predictive modeling, otherwise known as supervised learning, performance is directly tied to the model’s ability to exploit structure in the data and use that structure to make appropriate predictions. In general, we can further break down supervised learning into two more specific types, classification (predicting qualitative responses) and regression (predicting quantitative responses).

When we are evaluating classification problems, we will directly calculate the accuracy of a logistic regression model using a five-fold cross-validation:

# Example code for evaluating a classification problemfrom sklearn.linear_model import LogisticRegressionfrom sklearn.model_selection import cross_val_score ...