Overfitting and underfitting in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to split data into training and testing sets.

ML Python

from sklearn.model_selection import [1]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

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Atrain_test_split

Bcross_val_score

CGridSearchCV

DStandardScaler

Attempts:

3 left

2fill in blank

medium

Complete the code to create a simple linear regression model.

ML Python

from sklearn.linear_model import [1]

model = LinearRegression()

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ADecisionTreeClassifier

BLinearRegression

CLogisticRegression

DKNeighborsClassifier

Attempts:

3 left

3fill in blank

hard

Fix the error in the code to calculate mean squared error correctly.

ML Python

from sklearn.metrics import mean_squared_error

mse = mean_squared_error(y_true, [1])

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Ay_pred

By_train

CX_test

Dmodel

Attempts:

3 left

4fill in blank

hard

Fill both blanks to create a dictionary of squared errors for errors greater than 1.

ML Python

squared_errors = {i: (y_true[i] - y_pred[i])[1]2 for i in range(len(y_true)) if (y_true[i] - y_pred[i]) [2] 1}

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A**

B>

C<

D+

Attempts:

3 left

5fill in blank

hard

Fill all three blanks to create a dictionary of errors for predictions less than 0.5.

ML Python

errors = {i: y_true[i] [1] y_pred[i] for i in range(len(y_true)) if y_pred[i] [2] 0.5 and i [3] 10}

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A-

B<

C<=

D+

Attempts:

3 left