1. Which of the following is a key assumption made by the Naive Bayes classifier?
A. The features are correlated with each other
B. The features are independent of each other given the class label
C. The data follows a Gaussian distribution
D. The model uses a non-linear decision boundary
Answer: b) The features are independent of each other given the class label
Rationale: Naive Bayes assumes that all features are conditionally independent given
the class label, which simplifies the computation of probabilities.
2. Which of the following techniques can be used for feature selection?
A. Cross-validation
B. Recursive Feature Elimination (RFE)
C. K-means clustering
D. Dropout
Answer: b) Recursive Feature Elimination (RFE)
Rationale: RFE is a technique used to select important features by recursively
eliminating the least significant features based on model performance.
,3. What does the term "gradient descent" refer to in machine learning?
A. A method to find the optimal hyperparameters of the model
B. An algorithm to minimize the loss function by iteratively adjusting model
parameters
C. A technique to visualize data in a lower-dimensional space
D. A method to reduce overfitting
Answer: b) An algorithm to minimize the loss function by iteratively adjusting model
parameters
Rationale: Gradient descent is an optimization algorithm that adjusts the model’s
parameters to minimize the loss function during training.
4. 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.
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 "elbow method" used for in machine learning?
A. To find the optimal number of clusters in a clustering algorithm
B. To determine the best value for the learning rate
C. To evaluate the model's performance
D. To decide the best regularization parameter
Answer: a) To find the optimal number of clusters in a clustering algorithm
A. The features are correlated with each other
B. The features are independent of each other given the class label
C. The data follows a Gaussian distribution
D. The model uses a non-linear decision boundary
Answer: b) The features are independent of each other given the class label
Rationale: Naive Bayes assumes that all features are conditionally independent given
the class label, which simplifies the computation of probabilities.
2. Which of the following techniques can be used for feature selection?
A. Cross-validation
B. Recursive Feature Elimination (RFE)
C. K-means clustering
D. Dropout
Answer: b) Recursive Feature Elimination (RFE)
Rationale: RFE is a technique used to select important features by recursively
eliminating the least significant features based on model performance.
,3. What does the term "gradient descent" refer to in machine learning?
A. A method to find the optimal hyperparameters of the model
B. An algorithm to minimize the loss function by iteratively adjusting model
parameters
C. A technique to visualize data in a lower-dimensional space
D. A method to reduce overfitting
Answer: b) An algorithm to minimize the loss function by iteratively adjusting model
parameters
Rationale: Gradient descent is an optimization algorithm that adjusts the model’s
parameters to minimize the loss function during training.
4. 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.
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 "elbow method" used for in machine learning?
A. To find the optimal number of clusters in a clustering algorithm
B. To determine the best value for the learning rate
C. To evaluate the model's performance
D. To decide the best regularization parameter
Answer: a) To find the optimal number of clusters in a clustering algorithm
