Why advanced regression handles non-linearity in ML Python - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a polynomial feature transformer.

ML Python

from sklearn.preprocessing import [1]
poly = PolynomialFeatures(degree=2)

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APolynomialFeatures

BStandardScaler

CMinMaxScaler

DOneHotEncoder

Attempts:

3 left

💡 Hint

Common Mistakes

Using scalers or encoders instead of polynomial feature transformer.

The PolynomialFeatures class generates polynomial and interaction features, which help regression models capture non-linear relationships.

2fill in blank

medium

Complete the code to fit a linear regression model on polynomial features.

ML Python

from sklearn.linear_model import LinearRegression
model = LinearRegression()
X_poly = poly.fit_transform(X)
model.[1](X_poly, y)

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Afit

Btransform

Cscore

Dpredict

Attempts:

3 left

💡 Hint

Common Mistakes

Using predict instead of fit to train the model.

The fit method trains the model on the transformed polynomial features and target values.

3fill in blank

hard

Fix the error in the code to predict using the polynomial regression model.

ML Python

X_new_poly = poly.transform(X_new)
predictions = model.[1](X_new_poly)

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Atransform

Bpredict

Cfit

Dscore

Attempts:

3 left

💡 Hint

Common Mistakes

Using fit or transform instead of predict for predictions.

The predict method generates predictions from the trained model using new polynomial features.

4fill in blank

hard

Fill both blanks to create a dictionary of feature names and their coefficients from the model.

ML Python

feature_names = poly.get_feature_names_out(input_features)
coef_dict = [1](zip(feature_names, model.[2]_))

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Adict

Bcoef

Ccoef_

Dlist

Attempts:

3 left

💡 Hint

Common Mistakes

Using coef without underscore or wrong function to create dictionary.

The dict function creates a dictionary from zipped feature names and coefficients stored in coef_.

5fill in blank

hard

Fill both blanks to compute the mean squared error of the polynomial regression model predictions.

ML Python

from sklearn.metrics import [1]
y_pred = model.predict(X_poly)
mse = [2](y, y_pred)
print('Mean Squared Error:', mse)

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Amean_squared_error

Baccuracy_score

Cr2_score

Dconfusion_matrix

Attempts:

3 left

💡 Hint

Common Mistakes

Using classification metrics like accuracy or confusion matrix for regression.

The mean_squared_error function calculates the average squared difference between actual and predicted values, a common regression metric.