1. Which of the following is a supervised learning algorithm?
A. K-means
B. Support Vector Machine (SVM)
C. DBSCAN
D. K-medoids
Answer: b) Support Vector Machine (SVM)
Rationale: SVM is a supervised learning algorithm, which means it learns from
labeled data to classify new examples.
2. Which of the following is NOT an example of supervised learning?
A. Linear regression
B. Decision trees
C. K-means clustering
D. Naive Bayes
Answer: c) K-means clustering
Rationale: K-means is an unsupervised learning algorithm, as it works with unlabeled
data to group data into clusters.
,3. Which machine learning algorithm is most commonly used for classification
problems?
A. Linear regression
B. Logistic regression
C. K-means
D. Principal Component Analysis (PCA)
Answer: b) Logistic regression
Rationale: Logistic regression is widely used for binary classification tasks, predicting
categorical outcomes.
4. Which of the following is an example of an unsupervised learning algorithm?
A. Random Forest
B. K-nearest neighbors
C. K-means clustering
D. Linear regression
Answer: c) K-means clustering
Rationale: K-means clustering is an unsupervised learning algorithm used for
grouping data into clusters without prior labels.
, 5. In machine learning, overfitting occurs when:
A. The model performs well on the training data but poorly on new data.
B. The model performs poorly on both training and testing data.
C. The model performs well on both training and testing data.
D. The model is undertrained.
Answer: a) The model performs well on the training data but poorly on new data.
Rationale: Overfitting happens when a model learns not only the patterns but also the
noise in the training data, leading to poor generalization.
6. What is the purpose of cross-validation in machine learning?
A. To improve the performance of the training model
B. To divide the data into training and testing sets
C. To prevent overfitting by evaluating the model on multiple subsets of the data
D. To visualize the training process
Answer: c) To prevent overfitting by evaluating the model on multiple subsets of the
data
Rationale: Cross-validation helps assess the model's performance more reliably by
testing it on different data splits, ensuring it generalizes well.
A. K-means
B. Support Vector Machine (SVM)
C. DBSCAN
D. K-medoids
Answer: b) Support Vector Machine (SVM)
Rationale: SVM is a supervised learning algorithm, which means it learns from
labeled data to classify new examples.
2. Which of the following is NOT an example of supervised learning?
A. Linear regression
B. Decision trees
C. K-means clustering
D. Naive Bayes
Answer: c) K-means clustering
Rationale: K-means is an unsupervised learning algorithm, as it works with unlabeled
data to group data into clusters.
,3. Which machine learning algorithm is most commonly used for classification
problems?
A. Linear regression
B. Logistic regression
C. K-means
D. Principal Component Analysis (PCA)
Answer: b) Logistic regression
Rationale: Logistic regression is widely used for binary classification tasks, predicting
categorical outcomes.
4. Which of the following is an example of an unsupervised learning algorithm?
A. Random Forest
B. K-nearest neighbors
C. K-means clustering
D. Linear regression
Answer: c) K-means clustering
Rationale: K-means clustering is an unsupervised learning algorithm used for
grouping data into clusters without prior labels.
, 5. In machine learning, overfitting occurs when:
A. The model performs well on the training data but poorly on new data.
B. The model performs poorly on both training and testing data.
C. The model performs well on both training and testing data.
D. The model is undertrained.
Answer: a) The model performs well on the training data but poorly on new data.
Rationale: Overfitting happens when a model learns not only the patterns but also the
noise in the training data, leading to poor generalization.
6. What is the purpose of cross-validation in machine learning?
A. To improve the performance of the training model
B. To divide the data into training and testing sets
C. To prevent overfitting by evaluating the model on multiple subsets of the data
D. To visualize the training process
Answer: c) To prevent overfitting by evaluating the model on multiple subsets of the
data
Rationale: Cross-validation helps assess the model's performance more reliably by
testing it on different data splits, ensuring it generalizes well.
