1. 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.
2. What is a hyperparameter in machine learning?
A. A parameter that is learned from the training data
B. A parameter set during model training
C. A parameter that controls the model's training process
D. A parameter used to evaluate model performance
Answer: C) A parameter that controls the model's training process
,Rationale: Hyperparameters are parameters set before the training process (e.g.,
learning rate, number of trees in a random forest), which influence the model's
learning.
3. What is the difference between variance and bias in machine learning models?
A. Variance refers to the error introduced by the model's assumptions, while bias
refers to the variability in predictions across different datasets.
B. Variance refers to the error introduced by the model’s assumptions, while bias
refers to the variability of the model’s predictions on the same dataset.
C. Variance refers to the variability of the model's predictions, while bias refers to the
errors caused by oversimplifying the model.
D. There is no difference between variance and bias in machine learning.
Answer: C) Variance refers to the variability of the model's predictions, while bias
refers to the errors caused by oversimplifying the model.
Rationale: High variance indicates that the model is sensitive to fluctuations in the
training data (overfitting), while high bias indicates that the model is too simple and
does not capture the underlying patterns (underfitting).
4. Which of the following is used for dimensionality reduction?
, A. K-Nearest Neighbors (KNN)
B. Principal Component Analysis (PCA)
C. Naive Bayes
D. Logistic Regression
Answer: B) Principal Component Analysis (PCA)
Rationale: PCA is a technique used to reduce the dimensionality of a dataset while
retaining most of the variance.
5. In the context of decision trees, what does "pruning" refer to?
A. Increasing the tree depth to capture more complex patterns
B. Removing parts of the tree that do not contribute to prediction accuracy
C. Adding more features to the model
D. Dividing the dataset into smaller subsets
Answer: B) Removing parts of the tree that do not contribute to prediction accuracy
Rationale: Pruning helps avoid overfitting by simplifying the tree, removing branches
that have little impact on the model’s accuracy.
6. Which of the following is an example of a non-linear activation function?
A. Sigmoid
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.
2. What is a hyperparameter in machine learning?
A. A parameter that is learned from the training data
B. A parameter set during model training
C. A parameter that controls the model's training process
D. A parameter used to evaluate model performance
Answer: C) A parameter that controls the model's training process
,Rationale: Hyperparameters are parameters set before the training process (e.g.,
learning rate, number of trees in a random forest), which influence the model's
learning.
3. What is the difference between variance and bias in machine learning models?
A. Variance refers to the error introduced by the model's assumptions, while bias
refers to the variability in predictions across different datasets.
B. Variance refers to the error introduced by the model’s assumptions, while bias
refers to the variability of the model’s predictions on the same dataset.
C. Variance refers to the variability of the model's predictions, while bias refers to the
errors caused by oversimplifying the model.
D. There is no difference between variance and bias in machine learning.
Answer: C) Variance refers to the variability of the model's predictions, while bias
refers to the errors caused by oversimplifying the model.
Rationale: High variance indicates that the model is sensitive to fluctuations in the
training data (overfitting), while high bias indicates that the model is too simple and
does not capture the underlying patterns (underfitting).
4. Which of the following is used for dimensionality reduction?
, A. K-Nearest Neighbors (KNN)
B. Principal Component Analysis (PCA)
C. Naive Bayes
D. Logistic Regression
Answer: B) Principal Component Analysis (PCA)
Rationale: PCA is a technique used to reduce the dimensionality of a dataset while
retaining most of the variance.
5. In the context of decision trees, what does "pruning" refer to?
A. Increasing the tree depth to capture more complex patterns
B. Removing parts of the tree that do not contribute to prediction accuracy
C. Adding more features to the model
D. Dividing the dataset into smaller subsets
Answer: B) Removing parts of the tree that do not contribute to prediction accuracy
Rationale: Pruning helps avoid overfitting by simplifying the tree, removing branches
that have little impact on the model’s accuracy.
6. Which of the following is an example of a non-linear activation function?
A. Sigmoid
