Supervised Learning Algorithms MCQs
1. Which of the following is a supervised learning algorithm?
a. K-means clustering
b. Decision tree
c. Principal Component Analysis (PCA)
d. Apriori algorithm
Answer: b. Decision tree
2. In supervised learning, the training dataset consists of:
a. Input features only
b. Output labels only
c. Input features and output labels
d. None of the above
Answer: c. Input features and output labels
3. Which supervised learning algorithm is known for its ability to handle both classification and regression tasks?
a. Support Vector Machines (SVM)
b. Random Forest
c. K-nearest neighbors (KNN)
d. Linear Regression
Answer: b. Random Forest
4. The main objective of a classification algorithm in supervised learning is to:
a. Predict continuous values
b. Determine the optimal number of clusters
c. Assign input data to predefined categories or classes
d. Identify patterns in unlabeled data
Answer: c. Assign input data to predefined categories or classes
5. Which supervised learning algorithm is based on the concept of "nearest neighbors"?
a. K-means clustering
b. Decision tree
c. Naive Bayes
d. K-nearest neighbors (KNN)
Answer: d. K-nearest neighbors (KNN)
6. Which algorithm is used to minimize the errors between predicted and actual outputs in supervised learning?
a. Decision tree
b. Gradient Boosting
c. K-means clustering
d. Principal Component Analysis (PCA)
Answer: b. Gradient Boosting
7. Which algorithm is prone to overfitting in supervised learning?
a. Logistic Regression
b. Support Vector Machines (SVM)
c. K-means clustering
d. Linear Regression
Answer: a. Logistic Regression
8. Which supervised learning algorithm is an ensemble method that combines multiple weak learners to make predictions?
a. K-means clustering
b. Random Forest
c. Naive Bayes
d. K-nearest neighbors (KNN)
Answer: b. Random Forest
9. The term "supervised" in supervised learning refers to:
a. The presence of a teacher or supervisor during the learning process
b. The use of labeled data for training
c. The requirement of human supervision for algorithm execution
d. The need for a predefined set of rules for decision making
Answer: b. The use of labeled data for training
10. Which algorithm is suitable for handling multi-class classification problems in supervised learning?
a. Linear Regression
b. Decision tree
c. Naive Bayes
d. Multinomial Logistic Regression
Answer: b. Decision tree
11. Which supervised learning algorithm uses hyperplanes to separate classes?
a. K-means clustering
b. Random Forest
c. Support Vector Machines (SVM)
d. K-nearest neighbors (KNN)
Answer: c. Support Vector Machines (SVM)
12. Which supervised learning algorithm is based on the Bayes' theorem?
a. K-means clustering
b. Decision tree
c. Naive Bayes
d. Linear Regression
Answer: c. Naive Bayes
13. Which algorithm uses an ensemble of weak decision trees to make predictions?
a. K-means clustering
b. Random Forest
c. Na
ive Bayes
d. K-nearest neighbors (KNN)
Answer: b. Random Forest
14. Which supervised learning algorithm is used for time series forecasting?
a. Linear Regression
b. Decision tree
c. Recurrent Neural Networks (RNN)
d. Multinomial Logistic Regression
Answer: c. Recurrent Neural Networks (RNN)
15. Which algorithm is used for feature extraction in supervised learning?
a. Principal Component Analysis (PCA)
b. Decision tree
c. Naive Bayes
d. Linear Regression
Answer: a. Principal Component Analysis (PCA)
16. Which supervised learning algorithm is prone to the "curse of dimensionality"?
a. K-means clustering
b. Random Forest
c. K-nearest neighbors (KNN)
d. Support Vector Machines (SVM)
Answer: c. K-nearest neighbors (KNN)
17. Which algorithm is commonly used for sentiment analysis in supervised learning?
a. Linear Regression
b. Decision tree
c. Naive Bayes
d. Multinomial Logistic Regression
Answer: c. Naive Bayes
18. Which supervised learning algorithm aims to find the line that best fits the given data points?
a. Linear Regression
b. Decision tree
c. Support Vector Machines (SVM)
d. K-means clustering
Answer: a. Linear Regression
19. Which algorithm is used to reduce the dimensionality of the input features in supervised learning?
a. K-means clustering
b. Random Forest
c. Principal Component Analysis (PCA)
d. K-nearest neighbors (KNN)
Answer: c. Principal Component Analysis (PCA)
20. Which supervised learning algorithm is based on the concept of "error backpropagation"?
a. Linear Regression
b. Decision tree
c. Artificial Neural Networks (ANN)
d. Multinomial Logistic Regression
Answer: c. Artificial Neural Networks (ANN)
21. Which algorithm is used for imputing missing values in supervised learning?
a. K-means clustering
b. Random Forest
c. Support Vector Machines (SVM)
d. K-nearest neighbors (KNN)
Answer: d. K-nearest neighbors (KNN)
22. Which supervised learning algorithm is suitable for handling imbalanced datasets?
a. Logistic Regression
b. Decision tree
c. Naive Bayes
d. Random Forest
Answer: d. Random Forest
23. Which algorithm is used for collaborative filtering in supervised learning?
a. Linear Regression
b. Decision tree
c. K-nearest neighbors (KNN)
d. Multinomial Logistic Regression
Answer: c. K-nearest neighbors (KNN)
24. Which supervised learning algorithm uses a cost function based on the hinge loss?
a. Linear Regression
b. Decision tree
c. Support Vector Machines (SVM)
d. Naive Bayes
Answer: c. Support Vector Machines (SVM)
25. Which algorithm is used for time series forecasting with recurrent connections?
a. Linear Regression
b. Decision tree
c. Long Short-Term Memory (LSTM)
d. Multinomial Logistic Regression
Answer: c. Long Short-Term Memory (LSTM)
26. Which supervised learning algorithm is sensitive to outliers in the training data?
a. Logistic Regression
b. Decision tree
c. Support Vector Machines (SVM)
d. Random Forest
Answer
: b. Decision tree
27. Which algorithm is used for dimensionality reduction by preserving pairwise distances?
a. K-means clustering
b. Random Forest
c. Support Vector Machines (SVM)
d. t-distributed Stochastic Neighbor Embedding (t-SNE)
Answer: d. t-distributed Stochastic Neighbor Embedding (t-SNE)
28. Which supervised learning algorithm is used for text classification?
a. Linear Regression
b. Decision tree
c. Naive Bayes
d. Multinomial Logistic Regression
Answer: c. Naive Bayes
29. Which algorithm is used for detecting anomalies in supervised learning?
a. Linear Regression
b. Decision tree
c. Isolation Forest
d. K-means clustering
Answer: c. Isolation Forest
30. Which supervised learning algorithm is used for recommendation systems?
a. Linear Regression
b. Decision tree
c. Support Vector Machines (SVM)
d. Collaborative Filtering
Answer: d. Collaborative Filtering