Difference between Precision and Recall? How to select features? What’s p-value?

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Precision and Recall are evaluation metrics for classification models. Feature selection is important for model performance. P-value is a statistical measure.

Precision is the ratio of true positives to the total predicted positives. Recall is the ratio of true positives to the total actual positives.
Precision is important when false positives are costly, while recall is important when false negatives are costly.
Feature selection involves identifying the most relevant features for the model. This can be done using techniques like correlation analysis, feature importance, and stepwise regression.
P-value is a measure of the probability of obtaining a result as extreme as the observed result, assuming the null hypothesis is true. It is used to determine the statistical significance of a hypothesis test.
In medical field, precision and recall are important for evaluating diagnostic tests.
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Difference between Precision and Recall? How to select features? What’s p-value?

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