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:
1 2 3 |
event no-event event true positive false positive no-event false negative true negative |
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
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