Q. In a binary classification problem, what does a confusion matrix represent?
A.
The relationship between features
B.
The performance of the model on training data
C.
The true positive, false positive, true negative, and false negative counts
D.
The distribution of the target variable
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Solution
A confusion matrix summarizes the performance of a classification model by showing the counts of true positives, false positives, true negatives, and false negatives.
Correct Answer:
C
— The true positive, false positive, true negative, and false negative counts
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Q. In supervised learning, what is the role of the training set?
A.
To evaluate the model's performance
B.
To tune hyperparameters
C.
To train the model on labeled data
D.
To visualize the data
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Solution
The training set is used to train the model by providing it with labeled data, allowing it to learn the relationship between inputs and outputs.
Correct Answer:
C
— To train the model on labeled data
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Q. Which algorithm is commonly 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 method used to estimate the parameters in linear regression by minimizing the sum of the squares of the residuals.
Correct Answer:
C
— Ordinary Least Squares
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Q. Which metric is best suited for evaluating a model's performance on an imbalanced dataset?
A.
Accuracy
B.
Precision
C.
Recall
D.
F1 Score
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Solution
F1 Score is a better metric for imbalanced datasets as it considers both precision and recall, providing a balance between them.
Correct Answer:
D
— F1 Score
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Q. Which of the following algorithms is typically used for classification tasks?
A.
Linear Regression
B.
Logistic Regression
C.
K-Means Clustering
D.
Principal Component Analysis
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Solution
Logistic Regression is a classification algorithm used to predict binary outcomes based on input features.
Correct Answer:
B
— Logistic Regression
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Q. Which of the following is an example of a classification algorithm?
A.
Linear Regression
B.
Logistic Regression
C.
K-Means Clustering
D.
Principal Component Analysis
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Solution
Logistic Regression is a classification algorithm used to predict binary outcomes based on input features.
Correct Answer:
B
— Logistic Regression
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Q. Which of the following is NOT a type of supervised learning?
A.
Classification
B.
Regression
C.
Clustering
D.
Time Series Forecasting
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Solution
Clustering is an unsupervised learning technique, while classification and regression are types of supervised learning.
Correct Answer:
C
— Clustering
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