confusion matrix and finding the best parameter in a Decision tree.

Could you please help me in the interpretation of the confusion matrix and finding the best parameter in a Decision tree.

A confusion matrix is a technique for summarizing the performance of a classification algorithm.

we can assign the event row as “positive” and the no-event row as “negative“. We can then assign the event column of predictions as “true” and the no-event as “false“.

This gives us:

• “true positive” for correctly predicted event values.
• “false positive” for incorrectly predicted event values.
• “true negative” for correctly predicted no-event values.
• “false negative” for incorrectly predicted no-event values.

We can summarize this in the confusion matrix as follows:

  A hyperparameter is a parameter whose value is set before the learning process begins.

Tuning Strategies

We will explore two different methods for optimizing hyperparameters:

• Grid Search
• Randomized search