Q. In logistic regression, what is the output of the model?
A.
A continuous value
B.
A probability between 0 and 1
C.
A categorical label
D.
A binary decision tree
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Solution
Logistic regression outputs a probability between 0 and 1, which can be thresholded to classify data points into binary categories.
Correct Answer:
B
— A probability between 0 and 1
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Q. What is the main difference between logistic regression and linear regression?
A.
Logistic regression predicts continuous values, while linear regression predicts categorical values.
B.
Logistic regression is used for classification, while linear regression is used for regression tasks.
C.
Logistic regression requires more data than linear regression.
D.
There is no difference; they are the same.
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Solution
Logistic regression is used for binary classification tasks, while linear regression is used for predicting continuous values.
Correct Answer:
B
— Logistic regression is used for classification, while linear regression is used for regression tasks.
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Q. Which algorithm is typically used for linear regression?
A.
K-Nearest Neighbors
B.
Support Vector Machines
C.
Ordinary Least Squares
D.
Decision Trees
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Solution
Ordinary Least Squares (OLS) is a common algorithm used for linear regression to minimize the sum of squared differences between observed and predicted values.
Correct Answer:
C
— Ordinary Least Squares
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Q. Which of the following is a common method for handling imbalanced datasets in classification problems?
A.
Using a larger dataset
B.
Oversampling the minority class
C.
Reducing the number of features
D.
Using a simpler model
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Solution
Oversampling the minority class is a common method to address class imbalance, helping the model learn better from the underrepresented class.
Correct Answer:
B
— Oversampling the minority class
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Q. Which of the following techniques can be used to improve the performance of a classification model?
A.
Feature scaling
B.
Data augmentation
C.
Hyperparameter tuning
D.
All of the above
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Solution
All of the above techniques can be used to improve the performance of a classification model by enhancing data quality and model training.
Correct Answer:
D
— All of the above
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