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Which of the following is a ‘distance metric’ used in machine learning?
A. Euclidean Distance
B. Confusion Matrix
C. F1 Score
D. Precision
Answer: Euclidean Distance
What does ‘model overfitting’ mean?
A. The model performs well on training data but poorly on unseen data
B. The model performs poorly on both training and test data
C. The model performs well on test data but poorly on training data
D. The model is too simple to capture the underlying patterns in the data
Answer: The model performs well on training data but poorly on unseen data
What does ‘cross-validation’ help with?
A. Assessing how a model performs on different subsets of the data
B. Selecting the best features for the model
C. Scaling the features to a common range
D. Reducing the dimensionality of the data
Answer: Assessing how a model performs on different subsets of the data
Which algorithm is used for ‘dimensionality reduction’?
A. Principal Component Analysis (PCA)
B. K-Nearest Neighbors (KNN)
C. Naive Bayes
D. Support Vector Machine (SVM)
Answer: Principal Component Analysis (PCA)
What is ‘model interpretability’?
A. The degree to which a human can understand the decisions made by a model
B. The process of reducing the number of features in the dataset
C. The method of scaling features to a common range
D. The technique for clustering data points
Answer: The degree to which a human can understand the decisions made by a model
Which of the following is a ‘linear model’?
A. Linear Regression
B. K-means Clustering
C. Principal Component Analysis (PCA)
D. Naive Bayes
Answer: Linear Regression
What does ‘f1-score’ measure in classification tasks?
A. The harmonic mean of precision and recall
B. The ratio of true positives to the sum of true positives and false positives
C. The proportion of true positives in all actual positives
D. The accuracy of the model
Answer: The harmonic mean of precision and recall
What is ‘L1 regularization’ also known as?
A. Lasso Regression
B. Ridge Regression
C. Elastic Net
D. Principal Component Analysis (PCA)
Answer: Lasso Regression
Which of the following techniques is used for ‘feature scaling’?
A. Normalization
B. Dimensionality Reduction
C. Feature Extraction
D. Clustering
Answer: Normalization