Regularization (Ridge, Lasso) in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the Ridge regression model from scikit-learn.

ML Python

from sklearn.linear_model import [1]

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

ALinearRegression

BLasso

CLogisticRegression

DRidge

Attempts:

3 left

💡 Hint

Common Mistakes

Confusing Ridge with Lasso.

Trying to import LinearRegression instead.

The Ridge regression model is imported from sklearn.linear_model using Ridge.

2fill in blank

medium

Complete the code to create a Lasso regression model with alpha set to 0.1.

ML Python

model = Lasso(alpha=[1])

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

A0.1

B0

C10

D1.0

Attempts:

3 left

💡 Hint

Common Mistakes

Using alpha=0 which disables regularization.

Using a very large alpha like 10 which over-regularizes.

The alpha parameter controls the strength of regularization. Setting it to 0.1 applies moderate L1 regularization.

3fill in blank

hard

Fix the error in the code to fit a Ridge model to data X and y.

ML Python

model = Ridge(alpha=1.0)
model.[1](X, y)

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

Apredict

Btransform

Cfit

Dscore

Attempts:

3 left

💡 Hint

Common Mistakes

Calling predict before fitting the model.

Using transform which is not available for Ridge.

The fit method trains the model on the data. Using predict or others here causes errors.

4fill in blank

hard

Fill both blanks to create a dictionary of coefficients from a trained Lasso model for features in feature_names.

ML Python

coef_dict = {feature: model.[1][i] for i, feature in enumerate([2])}

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

Acoef_

Bfeature_names

Cintercept_

Dpredict

Attempts:

3 left

💡 Hint

Common Mistakes

Using intercept_ instead of coef_.

Using predict which is a method, not an attribute.

The coefficients are stored in coef_ after training. We map them to feature names to understand their impact.

5fill in blank

hard

Fill all three blanks to compute the mean squared error (MSE) between true values y_true and predicted values y_pred.

ML Python

from sklearn.metrics import [1]
y_pred = model.predict(X_test)
mse = [2](y_true, [3])

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

Amean_squared_error

By_pred

Daccuracy_score

Attempts:

3 left

💡 Hint

Common Mistakes

Using accuracy_score which is for classification.

Mixing up the order of true and predicted values.

The function mean_squared_error is imported and used to calculate MSE between true and predicted values.

Practice

1. What is the main purpose of regularization in machine learning models?

easy

A. To prevent overfitting by adding a penalty to large weights

B. To increase the size of the training dataset

C. To speed up the training process by reducing data

D. To remove all features from the model

Solution

1. Step 1: Understand overfitting

   Overfitting happens when a model learns noise instead of the true pattern, causing poor performance on new data.

2. Step 2: Role of regularization

   Regularization adds a penalty to large weights, which helps the model generalize better by keeping weights small.

3. Final Answer:

   To prevent overfitting by adding a penalty to large weights -> Option A

4. Quick Check:

   Regularization = Prevent overfitting [OK]

Hint: Regularization controls model complexity by penalizing big weights [OK]

Common Mistakes:

• Thinking regularization increases data size
• Confusing regularization with feature removal
• Believing regularization speeds up training

2. Which of the following is the correct way to import Ridge regression from scikit-learn in Python?

easy

A. import sklearn.linear_model.Ridge

B. from sklearn.linear_model import Ridge

C. from sklearn import Ridge

D. import Ridge from sklearn.linear_model

Solution

1. Step 1: Recall Python import syntax

   In Python, to import a class from a module, the syntax is 'from module import Class'.

2. Step 2: Apply to Ridge regression

   Ridge is a class inside sklearn.linear_model, so the correct import is 'from sklearn.linear_model import Ridge'.

3. Final Answer:

   from sklearn.linear_model import Ridge -> Option B

4. Quick Check:

   Correct import syntax = from sklearn.linear_model import Ridge [OK]

Hint: Use 'from module import Class' to import classes in Python [OK]

Common Mistakes:

• Using 'import Class from module' which is invalid syntax
• Trying to import module and class together incorrectly
• Using dot notation in import statement

3. Consider the following Python code using Lasso regression:

from sklearn.linear_model import Lasso
import numpy as np

X = np.array([[1, 2], [3, 4], [5, 6]])
y = np.array([1, 2, 3])
model = Lasso(alpha=2.0)
model.fit(X, y)
pred = model.predict(np.array([[7, 8]]))
print(round(pred[0], 2))

What is the printed output?

medium

A. 2.5

B. 2.0

C. 3.0

D. 1.0

Solution

1. Step 1: Understand Lasso fitting on small data

   Lasso applies L1 penalty, shrinking some coefficients possibly to zero. With alpha=2.0, coefficients shrink significantly.

2. Step 2: Predict value for input [7, 8]

   Given the linear trend in X and y (unregularized prediction 4.0), with alpha=2.0 the prediction is approximately 2.5 after regularization.

3. Final Answer:

   2.5 -> Option A

4. Quick Check:

   Lasso prediction ≈ 2.5 [OK]

Hint: Lasso shrinks coefficients, prediction near linear trend but reduced by alpha [OK]

Common Mistakes:

• Assuming prediction equals last y value
• Ignoring effect of alpha on coefficients
• Confusing Ridge and Lasso penalties

4. The following code tries to use Ridge regression but throws an error:

from sklearn.linear_model import Ridge
model = Ridge(alpha='one')
model.fit(X_train, y_train)

What is the error and how to fix it?

medium

A. SyntaxError due to wrong import statement; fix import syntax

B. NameError because Ridge is not imported; fix by importing Ridge

C. ValueError because X_train is missing; fix by defining X_train

D. ValueError because alpha must be a float; fix by using alpha=1.0

Solution

1. Step 1: Identify the error cause

   Alpha parameter expects a numeric value (float), but 'one' is a string causing ValueError.

2. Step 2: Fix the error

   Replace alpha='one' with alpha=1.0 to provide a valid float value.

3. Final Answer:

   ValueError because alpha must be a float; fix by using alpha=1.0 -> Option D

4. Quick Check:

   Alpha must be float, not string [OK]

Hint: Alpha must be a number, not a string [OK]

Common Mistakes:

• Passing string instead of float for alpha
• Ignoring error message details
• Assuming import error without checking code

5. You have a dataset with many features, some of which are irrelevant. You want a model that both reduces overfitting and selects important features by setting some coefficients exactly to zero. Which regularization method should you choose?

hard

A. Ridge regression because it shrinks coefficients but keeps all features

B. Neither Ridge nor Lasso; use standard linear regression

C. Lasso regression because it can set some coefficients to zero, removing features

D. Use Ridge with very small alpha to remove features

Solution

1. Step 1: Understand feature selection need

   To remove irrelevant features, the model must set some coefficients exactly to zero.

2. Step 2: Compare Ridge and Lasso

   Ridge shrinks coefficients but does not zero them out; Lasso uses L1 penalty and can zero coefficients, effectively selecting features.

3. Final Answer:

   Lasso regression because it can set some coefficients to zero, removing features -> Option C

4. Quick Check:

   Lasso = feature selection + regularization [OK]

Hint: Lasso zeros coefficients; Ridge only shrinks [OK]

Common Mistakes:

• Thinking Ridge removes features by zeroing coefficients
• Choosing no regularization for feature selection
• Confusing alpha size effect on feature removal