CODEWITHSUNDEEP
machine-learninglinear-regressionfeature-extractionfeature-selection
mudabbir ali
mudabbir ali

Reputation: 206

Explain feature interaction vs feature correlation

I am confused in the mentioned terminologies in machine learning paradigm? Can anybody drop some kind response here?. I shall be grateful to you..

Upvotes: 2

Views: 1154

Answers (1)

mudabbir ali
mudabbir ali

Reputation: 206

Feature correlation

means that some feature X1 and X2 are dependent to each other regardless of the target prediction Y. In other words we can say if I increase the value of X1 then X2 would also increase or decrease For Example: Features (Height(X1), Weight(X2) of a person and prediction variable RunningSpeed (Y) of a person). So if we increase the height then obviously weight will also increase.

Feature Interaction

on the other hand says what our model ouputs a prediction on the basis of independent features X1 and X2. In other words we can say what will be our output if X1 is selected only or what will be output if X2 is selected only or what will be the prediction if combination of both X1 + X2 is selected. This combination defines the interaction among feature. Such combination may be (+,-,*,/). For example: House size(X1) , House Location (X2) and Price (prediction Y). As we can see X1 and X2 are not correlated to each other but both of them contribute in making prediction of House price.

Upvotes: 2

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