Java: Data Science Made Easy

Evaluation

We have covered many machine learning libraries, and many of them implement the same algorithms such as random forest or logistic regression. Also, each individual model can have many different parameters, a logistic regression has the regularization coefficient, an SVM is configured by setting the kernel and its parameters.

How do we select the best single model out of so many possible variants?

For that, we first define some evaluation metric and then select the model which achieves the best possible performance with respect to this metric. For binary classification, there are many metrics that we can use for comparison, and the most commonly used ones are as follows:

• Accuracy and error
• Precision, recall, and F1
• AUC (AU ROC) ...