
STINTSY Quiz 2
Authored by Bryant Lee
Computers
University
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5 questions
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1.
MULTIPLE CHOICE QUESTION
1 min • 1 pt
In evaluating the performance of a linear regression model, which metric is more commonly used when you want to minimize the impact of large errors and also retain the original scale of the target variable?
Mean Sum of Squared Error
Coefficient of Determination
Root Mean Squared Error
Mean of Absolute Error
2.
MULTIPLE CHOICE QUESTION
1 min • 1 pt
In the context of supervised learning with linear regression, what is the primary objective when fitting a model to the training data?
Maximize the number of features in the model
Minimize the differences between predicted and actual target values
Ensure that the model perfectly fits the training data
Maximize the R-squared value regardless of other metrics
3.
MULTIPLE CHOICE QUESTION
1 min • 1 pt
In the context of training a linear regression model using gradient descent, what is the potential consequence of choosing a learning rate that is too high?
The model converges too slowly
The model converges to the optimal solution faster
The model performs better on unseen data
The model may fail to converge and oscillate around the minimum
4.
MULTIPLE CHOICE QUESTION
1 min • 1 pt
What is a key difference between stochastic gradient descent (SGD) and mini-batch gradient descent in the context of training a linear regression model?
SGD uses all training data at once, while mini-batch only uses a single sample
SGD updates weights after each data point, while mini-batch updates weights after a small subset of data
Mini-batch gradient descent is faster but less accurate than SGD
Mini-batch gradient descent always converges to the global minimum, while SGD does not
5.
MULTIPLE CHOICE QUESTION
1 min • 1 pt
Which of the following feature preprocessing steps is typically important before applying linear regression?
Scaling or normalizing numerical features
Adding polynomial features to increase dimensionality
Removing outliers without any consideration
Encoding continuous features as categorical
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