Challenge - 5 Problems

🎖️

Multiple Linear Regression Master

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Test your skills under time pressure!

🧠 Conceptual

intermediate

2:00remaining

Understanding the role of coefficients in multiple linear regression

In multiple linear regression, what does each coefficient represent?

AThe total variance explained by all features combined.

BThe average value of the target variable across all data points.

CThe change in the target variable for a one-unit change in the corresponding feature, holding other features constant.

DThe probability that the model predictions are correct.

Attempts:

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❓ Predict Output

intermediate

2:00remaining

Output of prediction with given coefficients and input

Given a multiple linear regression model with coefficients [2, -1, 0.5] and intercept 3, what is the predicted value for input features [4, 2, 6]?

ML Python

coefficients = [2, -1, 0.5]
intercept = 3
features = [4, 2, 6]
prediction = intercept + sum(c * f for c, f in zip(coefficients, features))
print(prediction)

A8.0

B12.0

C11.0

D10.0

Attempts:

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❓ Hyperparameter

advanced

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Choosing the right regularization for multiple linear regression

You want to reduce overfitting in a multiple linear regression model by adding a penalty on large coefficients. Which regularization method should you choose if you want some coefficients to become exactly zero?

ANo regularization

BL2 regularization (Ridge)

CEarly stopping

DL1 regularization (Lasso)

Attempts:

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❓ Metrics

advanced

2:00remaining

Interpreting R-squared in multiple linear regression

What does an R-squared value of 0.85 indicate about a multiple linear regression model?

A85% of the variance in the target variable is explained by the model's features.

BThe model's predictions are 85% accurate on new data.

CThe model has an 85% chance of making correct predictions.

DThe model's error rate is 15%.

Attempts:

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🔧 Debug

expert

2:00remaining

Identifying the error in multiple linear regression prediction code

What error will this code raise when predicting with a multiple linear regression model?

import numpy as np
coefficients = np.array([1.5, -2.0, 0.7])
intercept = 0.5
features = np.array([2, 3])
prediction = intercept + np.dot(coefficients, features)
print(prediction)

AValueError due to shape mismatch in np.dot

BNo error, prints a valid prediction

CTypeError because coefficients and features are different types

DNameError because np is not imported

Attempts:

2 left