Bias-variance tradeoff in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the mean squared error (MSE) between predictions and true values.

ML Python

mse = ((predictions - true_values) [1] 2).mean()

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A-

B+

C*

D**

Attempts:

3 left

2fill in blank

medium

Complete the code to split data into training and testing sets using scikit-learn.

ML Python

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=[1], random_state=42)

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A2

B0.2

C0.5

D20

Attempts:

3 left

3fill in blank

hard

Fix the error in the code to train a linear regression model and make predictions.

ML Python

from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.[1](X_train, y_train)
predictions = model.predict(X_test)

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Afit

Bscore

Cpredict

Dtrain

Attempts:

3 left

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps model complexity to training error.

ML Python

errors = {complexity: model_errors[complexity] [1] for complexity in model_errors if model_errors[complexity] [2] 0}

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A* 100

B>

C<

D+ 1

Attempts:

3 left

5fill in blank

hard

Fill the blanks to calculate bias, variance, and total error from predictions.

ML Python

bias = (([1] - true_values) ** 2).mean()
variance = predictions.var(axis=0).mean()
total_error = bias + [2]

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Apredictions.mean(axis=0)

Bvariance

Cbias

Dtrue_values

Attempts:

3 left