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Day 25 - How is Feature Importance Calculated? Quiz

Here’s your chance to prove what you learned

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Question 1 of 4

 

Which feature importance method would be most appropriate for a binary classification problem where interpretability is crucial?

A

Linear Regression

B

Logistic Regression

C

Random Forest

D

Principal Component Analysis

Question 2 of 4

 

How does SHAP differ from traditional feature importance calculations?

A

It only considers individual feature contributions

B

It ignores feature interactions

C

It considers both individual and collaborative feature effects

D

It only works with neural networks

Question 3 of 4

 

What's the main advantage of Random Forest over a single Decision Tree for feature importance?

A

It's faster to compute

B

It provides more stable and balanced results

C

It only requires one tree

D

It uses fewer features

Question 4 of 4

In calculating feature importance using the step-by-step method, why do we measure baseline performance first?

A

To speed up the calculation process

B

To establish a reference point for comparison

C

To eliminate unimportant features

D

To validate the model architecture

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