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

Recall & Review

beginner

What is feature importance in regression?

Feature importance in regression tells us how much each input feature (variable) helps the model predict the target value. It shows which features matter most.

beginner

Name one simple method to calculate feature importance in regression models.

One simple method is to look at the coefficients of a linear regression model. Larger absolute values mean the feature has more influence on the prediction.

intermediate

How does permutation importance work for regression models?

Permutation importance measures feature importance by randomly shuffling one feature's values and seeing how much the model's prediction error increases. A big increase means the feature is important.

intermediate

Why might feature importance from tree-based models be more reliable than from linear regression?

Tree-based models capture complex relationships and interactions between features, so their importance scores reflect more realistic influence, while linear regression assumes simple linear effects.

advanced

What is a limitation of using feature importance scores directly for feature selection?

Feature importance scores can be biased if features are correlated or if the model is complex. Removing features based only on importance might hurt model performance.

Which method uses model coefficients to determine feature importance in regression?

A Linear regression coefficients

B Permutation importance

C SHAP values

D PCA

Linear regression coefficients directly show how much each feature affects the prediction.

What does a large increase in error after shuffling a feature indicate in permutation importance?

A The feature is redundant

B The feature is not important

C The model is overfitting

D The feature is important

A large error increase means the model relies heavily on that feature.

Which model type often provides feature importance by measuring splits and gains?

A Decision trees

B K-means clustering

C Linear regression

D Naive Bayes

Decision trees measure feature importance by how much splits improve prediction.

Why can correlated features cause problems in feature importance?

A They always increase importance scores

B They can share importance, making scores unreliable

C They reduce model accuracy

D They are ignored by models

Correlated features can split importance, hiding true influence.

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

A Model coefficients

B Permutation importance

C Feature scaling

D Tree-based importance

Feature scaling changes feature values but does not measure importance.

Explain how permutation importance helps identify important features in regression models.

Think about what happens when you mix up one feature's values.

Describe why feature importance from tree-based regression models might be more informative than from linear regression.

Consider how trees split data compared to straight lines.

Practice

1. What does feature importance tell us in a regression model?

easy

A. The accuracy of the model on test data

B. The number of layers in the model

C. Which input features most influence the model's predictions

D. The speed at which the model trains

Solution

1. Step 1: Understand feature importance meaning

   Feature importance measures how much each input feature affects the model's output.

2. Step 2: Compare options to definition

   Only Which input features most influence the model's predictions correctly describes this concept as the influence of input features on predictions.

3. Final Answer:

   Which input features most influence the model's predictions -> Option C

4. Quick Check:

   Feature importance = input influence [OK]

Hint: Feature importance ranks inputs by their effect on output [OK]

Common Mistakes:

• Confusing feature importance with model accuracy
• Thinking it shows model speed or size
• Mixing it up with number of model layers

2. Which Python library function is commonly used to get feature importance from a Random Forest regression model?

easy

A. model.score()

B. model.predict()

C. model.fit()

D. model.feature_importances_

Solution

1. Step 1: Recall Random Forest attribute for feature importance

   Random Forest regression models in scikit-learn provide feature importance via the attribute feature_importances_.

2. Step 2: Eliminate other options

   predict() makes predictions, fit() trains the model, and score() evaluates performance, none return feature importance.

3. Final Answer:

   model.feature_importances_ -> Option D

4. Quick Check:

   Feature importance attribute = feature_importances_ [OK]

Hint: Feature importance in Random Forest is model.feature_importances_ [OK]

Common Mistakes:

• Using predict() instead of feature_importances_
• Confusing fit() with importance retrieval
• Trying to get importance from score()

3. Given this code snippet, what will be the output?

from sklearn.ensemble import RandomForestRegressor
import numpy as np

X = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])
y = np.array([10, 20, 30, 40])
model = RandomForestRegressor(random_state=0)
model.fit(X, y)
print(model.feature_importances_)

medium

A. [0.3 0.7]

B. [0.7 0.3]

C. [0.5 0.5]

D. [0.0 1.0]

Solution

1. Step 1: Understand the dataset and model

   The features are two columns with increasing values, but the first feature has a slightly stronger linear relationship with the target.

2. Step 2: Interpret feature importance output

   Random Forest will assign higher importance to the feature with stronger influence, which is the first feature here.

3. Final Answer:

   [0.7 0.3] -> Option B

4. Quick Check:

   Feature 1 stronger -> higher importance [OK]

Hint: Feature with stronger effect gets higher importance [OK]

Common Mistakes:

• Assuming equal importance without checking data
• Expecting exact 0.5 values
• Confusing feature importance with coefficients

4. You run this code but get an error:

from sklearn.ensemble import RandomForestRegressor
model = RandomForestRegressor()
print(model.feature_importances_)

What is the problem?

medium

A. The model must be trained with fit() before accessing feature_importances_

B. RandomForestRegressor does not have feature_importances_ attribute

C. You must import feature_importances_ separately

D. feature_importances_ is only for classification models

Solution

1. Step 1: Identify cause of error

   Accessing feature_importances_ before training causes an AttributeError because the model has no learned features yet.

2. Step 2: Correct usage

   Call fit() on training data before accessing feature_importances_.

3. Final Answer:

   The model must be trained with fit() before accessing feature_importances_ -> Option A

4. Quick Check:

   Train model first, then get feature importance [OK]

Hint: Always fit model before checking feature_importances_ [OK]

Common Mistakes:

• Trying to access feature_importances_ before fit()
• Thinking feature_importances_ needs separate import
• Believing it's only for classification

5. You have a regression dataset with 10 features. After training a Random Forest model, you find two features have very low importance scores close to zero. What is the best next step?

hard

A. Remove those low-importance features and retrain the model

B. Increase the number of trees in the forest

C. Add more features similar to the low-importance ones

D. Ignore feature importance and keep all features

Solution

1. Step 1: Interpret low feature importance meaning

   Features with near-zero importance contribute little to predictions and may add noise or complexity.

2. Step 2: Decide action based on importance

   Removing unimportant features simplifies the model and can improve performance and interpretability.

3. Final Answer:

   Remove those low-importance features and retrain the model -> Option A

4. Quick Check:

   Drop low-importance features to simplify model [OK]

Hint: Drop features with near-zero importance to improve model [OK]

Common Mistakes:

• Adding more similar low-importance features
• Ignoring importance and keeping all features
• Only increasing trees without feature selection