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

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding feature importance in decision trees

Which statement best describes how feature importance is calculated in a decision tree model?

A It counts how many times a feature appears in the dataset.

B It measures the average value of each feature across all samples.

C It calculates the correlation between each feature and the target variable.

D It measures how much each feature decreases the weighted impurity in the tree's splits.

💡 Hint

Think about how decision trees decide where to split data.

Decision trees calculate feature importance by summing how much each feature reduces impurity (like Gini or entropy) when used for splits, weighted by the number of samples it splits.

❓ Predict Output

intermediate

Output of feature importance extraction code

What is the output of the following Python code using scikit-learn's RandomForestClassifier?

ML Python

from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_iris

iris = load_iris()
X, y = iris.data, iris.target
model = RandomForestClassifier(random_state=0)
model.fit(X, y)
importances = model.feature_importances_
print([round(i, 2) for i in importances])

A [0.11, 0.03, 0.44, 0.42]

B [0.25, 0.25, 0.25, 0.25]

C [0.0, 0.0, 0.0, 1.0]

D [0.5, 0.2, 0.2, 0.1]

💡 Hint

Random forests usually assign higher importance to features that better split the data.

The output shows the relative importance of each feature in the iris dataset as computed by the random forest. Sepal length and width have lower importance compared to petal length and petal width.

❓ Hyperparameter

advanced

Effect of max_features on feature importance in Random Forest

How does setting the max_features parameter to a low value in a Random Forest affect the computed feature importance?

A It has no effect on feature importance values.

B It increases the importance of all features equally.

C It can reduce the importance of some features because fewer features are considered at each split.

D It causes the model to ignore the target variable.

💡 Hint

Think about how limiting features at splits changes the model's view of data.

When max_features is low, each tree sees fewer features at each split, so some features may be less likely to be chosen, lowering their importance.

❓ Metrics

advanced

Interpreting SHAP values for feature importance

Which statement correctly describes what SHAP values represent in feature importance analysis?

A SHAP values are random numbers assigned to features for visualization.

B SHAP values show how much each feature contributes to pushing a prediction from the average prediction to the actual prediction.

C SHAP values measure the correlation between features and the target variable.

D SHAP values count how many times a feature appears in the training data.

💡 Hint

SHAP values explain individual predictions by comparing to a baseline.

SHAP values quantify each feature's contribution to the difference between the model's average output and the specific prediction, helping explain model decisions.

🔧 Debug

expert

Identifying error in feature importance extraction code

What error will the following code raise when trying to get feature importances from a trained model?

ML Python

from sklearn.linear_model import LogisticRegression
from sklearn.datasets import load_iris

iris = load_iris()
X, y = iris.data, iris.target
model = LogisticRegression(max_iter=200)
model.fit(X, y)
print(model.feature_importances_)

A AttributeError: 'LogisticRegression' object has no attribute 'feature_importances_'

B ValueError: Invalid parameter 'feature_importances_'

C TypeError: 'feature_importances_' is not callable

D No error; prints feature importances as a list

💡 Hint

Not all models provide feature importance attributes.

LogisticRegression does not have a feature_importances_ attribute. This attribute exists in tree-based models like RandomForest but not in linear models.

Practice

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

easy

A. The time taken to train the model

B. Which features most affect the model's predictions

C. The accuracy of the model on test data

D. The total number of features in the dataset

Solution

1. Step 1: Understand the meaning of feature importance

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

2. Step 2: Compare options with definition

   Only Which features most affect the model's predictions correctly states that feature importance shows which features most affect predictions.

3. Final Answer:

   Which features most affect the model's predictions -> Option B

4. Quick Check:

   Feature importance = Most influential features [OK]

Hint: Feature importance ranks features by prediction impact [OK]

Common Mistakes:

• Confusing feature count with importance
• Mixing accuracy with feature importance
• Thinking it measures training time

2. Which of the following is the correct way to get feature importance from a trained Random Forest model in Python using scikit-learn?

easy

A. model.feature_importances_

B. model.get_feature_importance()

C. model.feature_importance_

D. model.importance_features

Solution

1. Step 1: Recall scikit-learn Random Forest attribute

   The correct attribute to access feature importance is feature_importances_ with an 's' and underscore at the end.

2. Step 2: Check each option

   model.feature_importances_ matches the exact attribute name. Others are incorrect or do not exist.

3. Final Answer:

   model.feature_importances_ -> Option A

4. Quick Check:

   Random Forest uses feature_importances_ [OK]

Hint: Remember the plural 'importances' with trailing underscore [OK]

Common Mistakes:

• Using singular 'feature_importance_'
• Calling a non-existent method
• Mixing attribute names

3. You have a dataset with features: Age, Income, and Number of Purchases. After training a Random Forest, the feature importances are: Age=0.1, Income=0.7, Number of Purchases=0.2. What does this tell you about your model?

easy

A. Age is the most important feature influencing predictions

B. All features are equally important

C. Number of Purchases has no effect on predictions

D. Income is the most important feature influencing predictions

Solution

1. Step 1: Interpret feature importance values

   Higher values mean stronger influence on model predictions. Income has 0.7, which is highest.

2. Step 2: Compare other features

   Age (0.1) and Number of Purchases (0.2) have lower importance, so less influence.

3. Final Answer:

   Income is the most important feature influencing predictions -> Option D

4. Quick Check:

   Highest importance = most influential feature [OK]

Hint: Highest importance value shows key feature [OK]

Common Mistakes:

• Thinking low values mean no effect
• Assuming equal importance despite values
• Confusing feature names

4. Given this code snippet:

from sklearn.ensemble import RandomForestClassifier
X = [[1, 2], [3, 4], [5, 6], [7, 8]]
y = [0, 1, 0, 1]
model = RandomForestClassifier(random_state=0)
model.fit(X, y)
importances = model.feature_importances_
print(importances)

What is the expected output?

medium

A. [0.5 0.5]

B. [0.7 0.3]

C. [0.0 1.0]

D. An error because data is too small

Solution

1. Step 1: Understand the dataset and model

   The dataset has two features with balanced classes. RandomForestClassifier will split importance roughly evenly.

2. Step 2: Predict feature importance values

   With simple data and no strong bias, feature importance will be close to equal, around 0.5 each.

3. Final Answer:

   [0.5 0.5] -> Option A

4. Quick Check:

   Balanced features = equal importance [OK]

Hint: Small balanced data leads to roughly equal importances [OK]

Common Mistakes:

• Expecting exact 0 or 1 values
• Assuming error due to small data
• Thinking importance sums to more than 1

5. You run this code but get an AttributeError:

from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier()
model.feature_importances_

What is the likely cause?

medium

A. You need to import a different library for feature importance

B. RandomForestClassifier does not have feature_importances_ attribute

C. The model is not trained yet before accessing feature_importances_

D. The attribute name is misspelled

Solution

1. Step 1: Check model usage

   The model is created but not trained with fit() before accessing feature_importances_.

2. Step 2: Understand attribute availability

   Feature importance is only available after training. Accessing it before fit causes AttributeError.

3. Final Answer:

   The model is not trained yet before accessing feature_importances_ -> Option C

4. Quick Check:

   Train model before feature_importances_ [OK]

Hint: Always call fit() before feature_importances_ [OK]

Common Mistakes:

• Assuming attribute doesn't exist
• Thinking import is missing
• Misspelling attribute name