Suppose that the following code has been executed in a Jupyter notebook. Match each of the following tasks with the corresponding code for that task. from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1) from sklearn import linear_model reg = linear_model.LinearRegression() [ Choose ] train the regression model on the reg.fit(X_train, y_train) training dataset reg.predict(X_test) reg.fit(X_test, y_test) generate predictions on the test reg.score(X_test, y_test) dataset reg.score(X_test, y_train) reg.score(X_train, y_train) determine the R-squared for the training dataset reg.predict(X_train, y_test) reg.predict(X_train) determine the R-squared for the test [ Choose ] dataset

Suppose that the following code has been executed in a Jupyter notebook. Match each of the following tasks with the corresponding code for that task. from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1) from sklearn import linear_model reg = linear_model.LinearRegression() [ Choose ] train the regression model on the reg.fit(X_train, y_train) training dataset reg.predict(X_test) reg.fit(X_test, y_test) generate predictions on the test reg.score(X_test, y_test) dataset reg.score(X_test, y_train) reg.score(X_train, y_train) determine the R-squared for the training dataset reg.predict(X_train, y_test) reg.predict(X_train) determine the R-squared for the test [ Choose ] dataset

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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Question

Suppose that the following code has been executed in a Jupyter notebook. Match
each of the following tasks with the corresponding code for that task.
from sklearn. model_selection import train_test_split
X_train, X_test, y_train, y_test =
train_test_split(X, y, test_size=0.1)
from sklearn import linear_model
linear_model.LinearRegression()
reg
[ Choose ]
reg.fit(X_train, y_train)
train the regression model on the
training dataset
reg.predict(X_test)
reg.fit(X_test, y_test)
generate predictions on the test
reg.score(X_test, y_test)
dataset
reg.score(X_test, y_train)
reg.score(X_train, y_train)
de
the R-squared for the
training dataset
reg.predict(X_train, y_test)
reg.predict(X_train)
determine the R-squared for the test
[ Choose ]
dataset
>

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