Python for Machine Learning - The Complete Beginners Course - Implementation in Python: Training the Model on the Traini

The video tutorial explains how to use the linear regression model from Sklearn. It begins by importing the linear regression class, then assigns it to a variable named 'regressor'. The tutorial proceeds to demonstrate how to fit the training dataset, X_train and Y_train, to the linear regression model using the 'regressor.fit' method, enabling the training process.

What is the first step in using the Linear Regression model from Sklearn?

• Train the model

• Import the Linear Regression class

• Evaluate the model

• Create a dataset

What is the purpose of assigning the Linear Regression class to a variable?

• To evaluate the model

• To initialize the model for later use

• To store the training data

• To visualize the data

Which method is used to fit the training data to the Linear Regression model?

• regressor.fit()

• regressor.evaluate()

• regressor.predict()

• regressor.transform()

What are the inputs to the regressor.fit() method?

• X_train and Y_test

• X_test and Y_train

• X_test and Y_test

• X_train and Y_train

What is the main goal of fitting the training dataset to the Linear Regression model?

• To clean the data

• To visualize the data

• To split the data

• To prepare the model for predictions