Q. In supervised learning, what does overfitting refer to?
-
A.
Model performs well on training data but poorly on unseen data
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B.
Model performs poorly on both training and unseen data
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C.
Model generalizes well to new data
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D.
Model is too simple to capture the underlying trend
Solution
Overfitting occurs when a model learns the training data too well, capturing noise and failing to generalize to new data.
Correct Answer:
A
— Model performs well on training data but poorly on unseen data
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Q. In supervised learning, what is the primary purpose of the training dataset?
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A.
To evaluate model performance
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B.
To make predictions on new data
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C.
To train the model on known outcomes
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D.
To visualize data distributions
Solution
The training dataset is used to train the model on known outcomes, allowing it to learn the relationship between input features and target labels.
Correct Answer:
C
— To train the model on known outcomes
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Q. What is a real-world application of supervised learning in healthcare?
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A.
Predicting patient readmission rates
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B.
Segmenting patients into groups
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C.
Identifying trends in medical research
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D.
Clustering similar diseases
Solution
Supervised learning can be used to predict patient readmission rates based on historical patient data.
Correct Answer:
A
— Predicting patient readmission rates
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Q. What is the main difference between regression and classification in supervised learning?
-
A.
Regression predicts continuous values, classification predicts discrete labels
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B.
Regression is unsupervised, classification is supervised
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C.
Regression uses neural networks, classification does not
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D.
There is no difference
Solution
The main difference is that regression predicts continuous values, while classification predicts discrete labels.
Correct Answer:
A
— Regression predicts continuous values, classification predicts discrete labels
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Q. What metric is commonly used to evaluate the performance of a classification model?
-
A.
Mean Squared Error
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B.
Accuracy
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C.
R-squared
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D.
Confusion Matrix
Solution
Accuracy is a common metric used to evaluate the performance of a classification model, indicating the proportion of correct predictions.
Correct Answer:
B
— Accuracy
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Q. What type of data is required for supervised learning?
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A.
Unlabeled data
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B.
Labeled data
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C.
Semi-labeled data
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D.
No data required
Solution
Supervised learning requires labeled data, where each input is associated with a corresponding output.
Correct Answer:
B
— Labeled data
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Q. Which algorithm is commonly used for multi-class classification problems?
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A.
Support Vector Machines
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B.
K-Means Clustering
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C.
Linear Regression
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D.
Decision Trees
Solution
Decision Trees are commonly used for multi-class classification problems, as they can handle multiple classes effectively.
Correct Answer:
D
— Decision Trees
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Q. Which application of supervised learning can help in diagnosing diseases?
-
A.
Predicting patient outcomes based on historical data
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B.
Clustering patients with similar symptoms
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C.
Generating synthetic medical images
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D.
Analyzing patient demographics
Solution
Predicting patient outcomes based on historical data is a direct application of supervised learning in diagnosing diseases.
Correct Answer:
A
— Predicting patient outcomes based on historical data
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Q. Which of the following is a common use of supervised learning in marketing?
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A.
Customer segmentation
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B.
Churn prediction
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C.
Market basket analysis
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D.
Anomaly detection
Solution
Churn prediction is a common application of supervised learning in marketing, helping businesses identify customers likely to leave.
Correct Answer:
B
— Churn prediction
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Q. Which of the following is an example of a regression task?
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A.
Classifying images of animals
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B.
Predicting the temperature for tomorrow
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C.
Segmenting customers based on behavior
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D.
Identifying fraudulent transactions
Solution
Predicting the temperature for tomorrow is a regression task as it involves predicting a continuous value.
Correct Answer:
B
— Predicting the temperature for tomorrow
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Q. Which of the following is NOT a typical use case for supervised learning?
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A.
Email filtering
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B.
Customer churn prediction
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C.
Market basket analysis
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D.
Credit scoring
Solution
Market basket analysis is typically an unsupervised learning task, while the others are examples of supervised learning applications.
Correct Answer:
C
— Market basket analysis
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Q. Which supervised learning algorithm is typically used for binary classification tasks?
-
A.
Linear Regression
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B.
Logistic Regression
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C.
K-Means Clustering
-
D.
Principal Component Analysis
Solution
Logistic Regression is a common algorithm used for binary classification tasks, predicting the probability of a binary outcome.
Correct Answer:
B
— Logistic Regression
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