Linear Regression-1

Which library in Python is commonly used for implementing Linear Regression?

• matplotlib

• numpy

• sklearn

• pandas

What function is used in sklearn to create a Linear Regression model?

• LinearModel()

• LinearRegression()

• CreateLinear()

• sklearn.linear()

Which cost function is most commonly used in Linear Regression?

• Mean Absolute Error (MAE)

• Root Mean Square Error (RMSE)

• Hinge Loss

• Mean Squared Error (MSE)

What is the role of the intercept in Linear Regression?

• Represents the rate of change in the dependent variable

• Represents the starting value of the dependent variable when all independent variables are zero

• Minimizes the error in predictions

• Represents the noise in the model

How can you split a dataset into training and testing sets in Python?

• Using train_test_split from sklearn.model_selection

• Using train_test from sklearn.preprocessing

• Using split_data from sklearn.metrics

• Using data_splitter from sklearn.linear_model

What is the formula for a simple Linear Regression model?

• y=mx+c

• y=mx²+c

• y=ax²+bx+c

• y=mx−c

Which Python library is commonly used for data preprocessing before applying Linear Regression?

• OpenCV

• Scipy

• Pandas

• TensorFlow

Which of the following scenarios indicates overfitting in a Linear Regression model?

• Low training error and high test error

• Low training error and low test error

• High training error and low test error

• High training error and high test error

When using Linear Regression, how do you handle categorical variables?

• By applying gradient descent

• By ignoring them

• By adding noise to them

• By encoding them using techniques like one-hot encoding

In Linear Regression, what does the term "independent variable" refer to?

• The variable you are trying to predict

• The variables that influence the dependent variable

• The noise in the data

• A variable with no effect on the outcome