Feature importance in regression in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the correct model for linear regression.

ML Python

from sklearn.linear_model import [1]

Drag options to blanks, or click blank then click option'

ALinearRegression

BRandomForestRegressor

CLogisticRegression

DKNeighborsRegressor

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing classification models like LogisticRegression.

Using tree-based models when the task is simple linear regression.

The LinearRegression class is used to create a linear regression model in scikit-learn.

2fill in blank

medium

Complete the code to fit the linear regression model on features X and target y.

ML Python

model = LinearRegression()
model.[1](X, y)

Drag options to blanks, or click blank then click option'

Atransform

Bpredict

Cfit

Dscore

Attempts:

3 left

💡 Hint

Common Mistakes

Using predict before fitting the model.

Using transform which is for data preprocessing.

The fit method trains the model using the input features X and target y.

3fill in blank

hard

Fix the error in accessing the feature importance from a linear regression model.

ML Python

importance = model.[1]

Drag options to blanks, or click blank then click option'

Acoef_

Bfeature_coef_

Cimportance_

Dfeature_importances_

Attempts:

3 left

💡 Hint

Common Mistakes

Using feature_importances_ which is for tree models.

Trying to access a non-existent attribute.

For linear regression, the coefficients are stored in coef_, which represent feature importance.

4fill in blank

hard

Fill both blanks to create a dictionary mapping feature names to their importance values.

ML Python

feature_importance = { [1]: [2] for [1], [2] in zip(feature_names, importance) }

Drag options to blanks, or click blank then click option'

Aname

Bimportance

Ccoef_

Dvalue

Attempts:

3 left

💡 Hint

Common Mistakes

Using the same variable for both key and value.

Using attribute names instead of variable names.

We use name as the key and importance as the value in the dictionary comprehension.

5fill in blank

hard

Fill all three blanks to print the feature importance sorted by absolute value descending.

ML Python

sorted_features = sorted(feature_importance.items(), key=lambda x: abs(x[1]), reverse=[2])
for feature, importance in sorted_features:
    print(f"Feature: {feature}, Importance: {importance:.2f}")

Drag options to blanks, or click blank then click option'

A[1]

BTrue

CFalse

D[0]

Attempts:

3 left

💡 Hint

Common Mistakes

Sorting by the feature name instead of importance.

Setting reverse to False which sorts ascending.

We sort by the absolute value of the importance which is at index 1 in the tuple, and set reverse to True for descending order.

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