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I have a dataset consisting of 4 classes and around 200 features. I have implemented a Gaussian Naive Bayes classifier. I want now calculate the importance of each feature for each pair of classes according to the Gaussian Naive Bayes classifier. In the end, I want to visualize the 10 most important features for each pair of classes. That means for class 1 vs class 2, I want the importance of feature 1, feature 2, etc.

My proposal would be to calculate $\log\frac{P(X_n|Y=i)}{P(X_n|Y=j)}$ for each pair of classes.

I have calculated the mean and variance for each feature and each class. That means I have a mean and variance for each of the 200 features and each of the 4 classes. Taking the normal distribution I can classify a new data point.

How would you now calculate the feature importance measure?

Let's say we calculate $\log\frac{P(X_1|Y=1)}{P(X_1|Y=2)}$, that means the first feature ($X_1$) and class 1 and class 2, so it should give the importance of the first feature for class 1 and class 2.

I have both the normal distribution for the first feature for class 1 and class 2 but how should I calculate the probability, i.e., at which point should I evaluate the normal distribution?

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