1. Which of the following is not a type of machine learning?
A. Supervised learning
B. Unsupervised learning
C. Reinforcement learning
D. Structured learning
Answer: D) Structured learning
Rationale: Structured learning is not a recognized category of machine learning. The
main types are supervised, unsupervised, and reinforcement learning.
2. Which of the following algorithms is used for classification problems?
A. Linear Regression
B. K-Nearest Neighbors (KNN)
C. K-Means Clustering
D. Principal Component Analysis (PCA)
Answer: B) K-Nearest Neighbors (KNN)
Rationale: KNN is used for classification, while Linear Regression is used for
regression tasks. K-Means is for clustering, and PCA is for dimensionality reduction.
,3. What does the term “overfitting” mean in machine learning?
A. The model is too simple to make any predictions.
B. The model performs well on both the training and test datasets.
C. The model learns the noise in the training data and performs poorly on unseen data.
D. The model is unable to learn from the training data.
Answer: C) The model learns the noise in the training data and performs poorly on
unseen data.
Rationale: Overfitting occurs when a model becomes too complex, capturing the noise
in the training data, which causes poor performance on new, unseen data.
4. Which of the following is an example of an unsupervised learning technique?
A. Logistic Regression
B. Decision Trees
C. K-Means Clustering
D. Linear Regression
Answer: C) K-Means Clustering
Rationale: K-Means is an unsupervised learning algorithm used for clustering, while
the others are supervised learning algorithms.
, 5. In which scenario would you use a time series analysis?
A. Predicting the price of a stock based on its historical prices
B. Grouping customers based on purchasing behavior
C. Classifying email as spam or not spam
D. Recommending movies based on user preferences
Answer: A) Predicting the price of a stock based on its historical prices
Rationale: Time series analysis is specifically used to analyze data points ordered in
time, like predicting stock prices.
6. What does the term "precision" refer to in the context of classification models?
A. The proportion of correctly predicted positive instances out of all positive
predictions.
B. The proportion of correctly predicted negative instances out of all negative
predictions.
C. The overall accuracy of the model.
D. The ability of the model to generalize well to unseen data.
Answer: A) The proportion of correctly predicted positive instances out of all positive
predictions.
A. Supervised learning
B. Unsupervised learning
C. Reinforcement learning
D. Structured learning
Answer: D) Structured learning
Rationale: Structured learning is not a recognized category of machine learning. The
main types are supervised, unsupervised, and reinforcement learning.
2. Which of the following algorithms is used for classification problems?
A. Linear Regression
B. K-Nearest Neighbors (KNN)
C. K-Means Clustering
D. Principal Component Analysis (PCA)
Answer: B) K-Nearest Neighbors (KNN)
Rationale: KNN is used for classification, while Linear Regression is used for
regression tasks. K-Means is for clustering, and PCA is for dimensionality reduction.
,3. What does the term “overfitting” mean in machine learning?
A. The model is too simple to make any predictions.
B. The model performs well on both the training and test datasets.
C. The model learns the noise in the training data and performs poorly on unseen data.
D. The model is unable to learn from the training data.
Answer: C) The model learns the noise in the training data and performs poorly on
unseen data.
Rationale: Overfitting occurs when a model becomes too complex, capturing the noise
in the training data, which causes poor performance on new, unseen data.
4. Which of the following is an example of an unsupervised learning technique?
A. Logistic Regression
B. Decision Trees
C. K-Means Clustering
D. Linear Regression
Answer: C) K-Means Clustering
Rationale: K-Means is an unsupervised learning algorithm used for clustering, while
the others are supervised learning algorithms.
, 5. In which scenario would you use a time series analysis?
A. Predicting the price of a stock based on its historical prices
B. Grouping customers based on purchasing behavior
C. Classifying email as spam or not spam
D. Recommending movies based on user preferences
Answer: A) Predicting the price of a stock based on its historical prices
Rationale: Time series analysis is specifically used to analyze data points ordered in
time, like predicting stock prices.
6. What does the term "precision" refer to in the context of classification models?
A. The proportion of correctly predicted positive instances out of all positive
predictions.
B. The proportion of correctly predicted negative instances out of all negative
predictions.
C. The overall accuracy of the model.
D. The ability of the model to generalize well to unseen data.
Answer: A) The proportion of correctly predicted positive instances out of all positive
predictions.
