Feature importance explanation in ML Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is feature importance in machine learning?

Feature importance tells us how much each input feature helps the model make good predictions. It shows which features matter most.

beginner

Name one simple way to calculate feature importance.

One simple way is to look at how much the model's accuracy drops when we shuffle or remove a feature. Bigger drops mean more important features.

beginner

Why is understanding feature importance useful?

It helps us trust the model, find key factors in data, and sometimes improve the model by focusing on important features.

intermediate

What is a common method to get feature importance from tree-based models?

Tree-based models like Random Forests use how much each feature reduces error or impurity in splits to measure importance.

beginner

What does it mean if a feature has zero importance?

It means the feature does not help the model make better predictions and can often be removed without hurting performance.

Which feature importance method measures importance by shuffling feature values and checking accuracy drop?

A Coefficient magnitude

B Permutation importance

C PCA

D Gradient descent

Permutation importance shuffles a feature's values and measures how much the model's accuracy decreases.

In tree models, feature importance is often based on:

A The feature's mean value

B The feature's data type

C The feature's correlation with the target

D How often a feature is used to split nodes and how much it reduces error

Tree models calculate importance by how much a feature helps split data to reduce error or impurity.

If a feature has zero importance, what should you consider?

A Adding more features like it

B Doubling its values

C Removing it from the model

D Ignoring the model

Features with zero importance do not help predictions and can be removed to simplify the model.

Why might feature importance help in real life?

A To understand which factors affect outcomes the most

B To make the model slower

C To hide data from users

D To increase data size

Feature importance helps identify key factors influencing predictions, aiding understanding and trust.

Which of these is NOT a way to get feature importance?

A Feature scaling

B Tree split importance

C Permutation importance

D Coefficient size in linear models

Feature scaling changes feature values but does not measure importance.

Explain in your own words what feature importance means and why it matters.

Think about which features help the model make better guesses.

Describe one method to calculate feature importance and how you would interpret its results.

Consider shuffling feature values and checking model accuracy.