Q. In supervised learning, what is the primary goal of regression algorithms?
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A.
To classify data into categories
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B.
To predict continuous outcomes
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C.
To cluster similar data points
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D.
To reduce dimensionality
Solution
The primary goal of regression algorithms in supervised learning is to predict continuous outcomes based on input features.
Correct Answer:
B
— To predict continuous outcomes
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Q. What is a common application of supervised learning in finance?
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A.
Stock price prediction
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B.
Image recognition
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C.
Customer segmentation
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D.
Anomaly detection
Solution
Supervised learning is often used in finance for stock price prediction, where historical data is used to predict future prices.
Correct Answer:
A
— Stock price prediction
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Q. What is a potential application of supervised learning in marketing?
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A.
Customer segmentation
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B.
Predicting customer purchase behavior
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C.
Market basket analysis
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D.
Topic modeling
Solution
Predicting customer purchase behavior is a potential application of supervised learning in marketing, helping businesses tailor their strategies.
Correct Answer:
B
— Predicting customer purchase behavior
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Q. What type of supervised learning would you use to predict whether a patient has a disease based on their symptoms?
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A.
Regression
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B.
Clustering
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C.
Classification
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D.
Dimensionality Reduction
Solution
Classification is used to predict whether a patient has a disease based on their symptoms, as it involves categorizing outcomes.
Correct Answer:
C
— Classification
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Q. Which algorithm is commonly used for binary classification tasks?
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A.
Linear Regression
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B.
Logistic Regression
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C.
K-Means Clustering
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D.
Principal Component Analysis
Solution
Logistic Regression is commonly used for binary classification tasks, as it models the probability of a binary outcome.
Correct Answer:
B
— Logistic Regression
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Q. Which metric is commonly used to evaluate the performance of a classification model?
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A.
Mean Squared Error
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B.
Accuracy
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C.
R-squared
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D.
Silhouette Score
Solution
Accuracy is a common metric used to evaluate the performance of classification models, indicating the proportion of correct predictions.
Correct Answer:
B
— Accuracy
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Q. Which of the following best describes supervised learning?
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A.
Learning from unlabeled data
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B.
Learning from labeled data
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C.
Learning without feedback
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D.
Learning through reinforcement
Solution
Supervised learning involves learning from labeled data, where the model is trained on input-output pairs.
Correct Answer:
B
— Learning from labeled data
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Q. Which of the following is a classification problem in supervised learning?
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A.
Predicting house prices
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B.
Classifying emails as spam or not spam
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C.
Forecasting sales revenue
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D.
Estimating customer lifetime value
Solution
Classifying emails as spam or not spam is a classification problem, as it involves categorizing data into discrete classes.
Correct Answer:
B
— Classifying emails as spam or not spam
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Q. Which of the following is an example of a regression application?
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A.
Predicting customer churn
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B.
Estimating the price of a house
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C.
Identifying fraudulent transactions
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D.
Classifying images of animals
Solution
Estimating the price of a house is an example of a regression application, as it involves predicting a continuous value.
Correct Answer:
B
— Estimating the price of a house
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