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Multiple Linear Regression and Gradient Descent
we will solve quiz related to Multiple Linear Regression and Gradient Descent
Question 1
What is the objective of Multiple Linear Regression?
To find the relationship between a single independent variable and a dependent variable
To find the relationship between multiple independent variables and a dependent variable
To cluster data points
To reduce dimensionality
Question 2
Which of the following is the equation for Multiple Linear Regression?
y=mx+c
y=θ0+θ1x1+θ2x2+...+θnxn
y=ax2+bx+c
y=ex
Question 3
In Gradient Descent what is the purpose of the learning rate (α)?
It controls how quickly the model learns
It defines the number of features
It normalizes the dataset
It determines the output variable
Question 4
What is typically used as the cost function in Linear Regression with Gradient Descent?
Mean Absolute Error
Cross-Entropy Loss
Log Loss
Mean Squared Error
Question 5
What does the gradient represent in Gradient Descent?
Training accuracy
The shape of the model curve
Rate of change of the cost with respect to parameters
Number of data points
Question 6
Why do we normalize data before applying Gradient Descent?
To remove outliers
To improve convergence speed
To increase the number of features
To decrease accuracy
Question 7
If a regression model has high training accuracy but low test accuracy, what problem does it indicate?
Underfitting
Overfitting
Proper Generalization
No issues
Question 8
What is the primary challenge in using Multiple Linear Regression?
Handling multiple independent variables
Making predictions
Identifying multicollinearity among independent variables
Applying the regression equation
Question 9
What does a high R-squared value indicate in Multiple Linear Regression?
The model does not fit the data well
The independent variables explain a large portion of the variance in the dependent variable
The model is underfitting
The model has too many independent variables
There are 9 questions to complete.
