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Quiz 3: Linear Models and Optimization
Test your knowledge on Linear Regression, Loss Functions, and Gradient Descent.
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1. What does Linear Regression aim to model?
The relationship between dependent and independent variables
The variance of data points
Non-linear patterns in data
The classification of data
2. What is the primary objective of Linear Regression?
To maximize the slope of the line
To minimize the Mean Squared Error (MSE)
To increase the number of data points
To classify data into categories
3. What is a loss function in machine learning?
A function that measures the discrepancy between predicted and actual values
A function used to calculate gradients
A function that normalizes data
A function that splits the dataset into train and test sets
4. Which loss function is most sensitive to outliers?
Mean Squared Error (MSE)
Mean Absolute Error (MAE)
Cross-Entropy Loss
Huber Loss
5. Which loss function is commonly used for classification problems?
Mean Squared Error (MSE)
Cross-Entropy Loss
Mean Absolute Error (MAE)
Hinge Loss
6. What is the primary goal of Gradient Descent?
To find the maximum value of a function
To minimize the cost function
To calculate the slope of a line
To increase the dataset size
7. What does the learning rate control in Gradient Descent?
The speed of convergence
The size of the dataset
The type of loss function used
The number of iterations
8. What happens if the learning rate is too high in Gradient Descent?
The algorithm converges faster
The algorithm may overshoot the minimum
The algorithm stops prematurely
The algorithm runs indefinitely
9. What is the role of the gradient in Gradient Descent?
To point towards the steepest ascent
To point towards the steepest descent
To normalize the data
To calculate the slope of a line
10. Which parameter of Gradient Descent affects the step size?
Cost function
Learning rate
Batch size
Number of features
11. What is Mean Squared Error (MSE) used for?
Measuring accuracy in classification tasks
Measuring variance in clustering
Measuring error in regression tasks
Scaling features in data preprocessing
12. Which loss function increases linearly with the error?
Mean Squared Error (MSE)
Mean Absolute Error (MAE)
Cross-Entropy Loss
Log Loss
13. What is the optimal condition for Gradient Descent to stop?
When the gradient approaches zero
When the cost function reaches its maximum
When the number of iterations is exceeded
When the dataset is normalized
14. Which visualization helps understand the Gradient Descent process?
Scatter plot
Cost function curve
Box plot
Confusion matrix
15. What is the key difference between MSE and MAE?
MSE penalizes larger errors more than MAE
MSE is used for classification tasks, and MAE is used for regression
MSE is less sensitive to outliers compared to MAE
MSE is faster to compute than MAE
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