Feature importance in regression in ML Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding feature importance methods

Which method directly measures the impact of each feature on the prediction error by randomly shuffling its values and observing the change in model performance?

A Feature scaling

B Permutation importance

C Correlation coefficient with target variable

D Coefficient magnitude in linear regression

💡 Hint

Think about a method that tests how much the model's error increases when a feature's information is disrupted.

Permutation importance measures feature importance by shuffling feature values and checking how much the model's error increases, showing the feature's impact on predictions.

❓ Predict Output

intermediate

Output of feature importance extraction

What is the output of the following Python code using scikit-learn's RandomForestRegressor to get feature importances?

ML Python

from sklearn.ensemble import RandomForestRegressor
import numpy as np

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

A [0.3 0.7]

B [0.0 1.0]

C [0.5 0.5]

D [0.7 0.3]

💡 Hint

Random forests usually assign importance based on how much each feature reduces error across trees.

The output shows the relative importance of each feature. Here, the second feature has higher importance, approximately 0.7, and the first about 0.3.

❓ Model Choice

advanced

Choosing a model for interpretable feature importance

You want a regression model that provides clear, direct coefficients to interpret feature importance easily. Which model should you choose?

A Support vector regression

B Random forest regression

C Neural network regression

D Linear regression

💡 Hint

Look for a model where feature importance is given by simple coefficients.

Linear regression provides coefficients that directly show the effect of each feature on the output, making it easy to interpret feature importance.

❓ Metrics

advanced

Evaluating feature importance with model metrics

Which metric change best indicates a feature is important when removed from a regression model?

A Significant increase in mean squared error (MSE)

B Increase in training time

C Decrease in R-squared value

D Decrease in number of features

💡 Hint

Think about how prediction error changes when a feature is removed.

If removing a feature causes the mean squared error to increase significantly, it means the feature was important for accurate predictions.

🔧 Debug

expert

Debugging incorrect feature importance values

You trained a linear regression model but the feature importance (coefficients) are all zero. What is the most likely cause?

A The learning rate was set too high

B The features are perfectly correlated with each other

C The target variable has zero variance (all values are the same)

D The model was trained with too many epochs

💡 Hint

Consider what happens if the target does not change at all.

If the target variable has no variance, the model cannot learn any relationship, resulting in zero coefficients for all features.

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