Problem statementSend feedbackWhich of the following class of the sklearn library can be used to convert the independent features of a dataset into various degrees of polynomial features?

What is the correct sklearn syntax to add a third degree polynomial to your model? 1 pointpolyFeat = polyFeat.add(degree=3) polyFeat = polyFeat.fit(degree=3)polyFeat = PolynomialFeatures(degree=3)polyFeat = polyFeat.transform(degree=3)

Polynomial regression can model nonlinear relationships by transforming the features.Group of answer choicesTrueFalse

You are refining a logistic regression model to predict customer churn. The dataset includes various customer interaction metrics. To enhance your model, explore how polynomial features can improve prediction accuracy. This approach allows the model to capture complex interactions between variables.Here is your setup:from sklearn.datasets import make_classificationfrom sklearn.model_selection import train_test_splitfrom sklearn.linear_model import LogisticRegressionfrom sklearn.preprocessing import PolynomialFeatures# Generate synthetic data for binary classificationX, y = make_classification(n_samples=1000, n_features=3, n_classes=2, random_state=42)# Split the data into training and testing setsX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)# Apply polynomial features manuallypoly = PolynomialFeatures(degree=2)X_train_poly = poly.fit_transform(X_train)X_test_poly = poly.transform(X_test)What is the correct procedure to fit a logistic regression model on the training data after transforming it with polynomial features, and how should predictions be made on the test data?model = LogisticRegression()model.fit(X_train, y_train)y_pred = model.predict(X_test)model = LogisticRegression()model.fit(X_test_poly, y_test)y_pred = model.predict(X_train_poly)model = LogisticRegression()model.fit(X_train_poly, y_train)y_pred = model.predict(X_test_poly)model = LogisticRegression()model.fit(X_train_poly, y_test)y_pred = model.predict(X_test_poly)

What type of regression is used when the response variable is modeled as an nth degree polynomial of the input feature?

Which fundamental problems are being solved by adding non-linear patterns, such as polynomial features, to a standard linear approach?1 pointPrediction. Interpretation. Prediction and Interpretation. None of the above.