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What does ‘model validation’ involve?
A. Evaluating a model's performance on a validation set to check its generalization ability
B. Creating new features from existing data
C. Reducing the number of features in the dataset
D. Scaling features to a common range
Answer: Evaluating a model's performance on a validation set to check its generalization ability
Which algorithm is used for ‘online learning’?
A. Stochastic Gradient Descent
B. Principal Component Analysis (PCA)
C. K-means Clustering
D. Naive Bayes
Answer: Stochastic Gradient Descent
What is ‘bagging’?
A. An ensemble method that reduces variance by averaging predictions from multiple models
B. A technique for dimensionality reduction
C. A method for scaling features
D. A technique for feature extraction
Answer: An ensemble method that reduces variance by averaging predictions from multiple models
Which of the following is a type of ‘clustering’ algorithm?
A. K-means Clustering
B. Logistic Regression
C. Linear Regression
D. Decision Trees
Answer: K-means Clustering
Which of the following is a ‘distance-based’ algorithm?
A. K-Nearest Neighbors (KNN)
B. Support Vector Machine (SVM)
C. Decision Trees
D. Naive Bayes
Answer: K-Nearest Neighbors (KNN)
What does ‘recall’ measure in classification tasks?
A. The proportion of true positives out of all actual positives
B. The ratio of true positives to the sum of true positives and false positives
C. The accuracy of the model
D. The harmonic mean of precision and recall
Answer: The proportion of true positives out of all actual positives
What is ‘stochastic gradient descent’?
A. An optimization algorithm used for training machine learning models
B. A method for dimensionality reduction
C. A technique for feature selection
D. A method for scaling features
Answer: An optimization algorithm used for training machine learning models
Which algorithm is used for ‘decision boundaries’?
A. Support Vector Machine (SVM)
B. Principal Component Analysis (PCA)
C. K-Nearest Neighbors (KNN)
D. Naive Bayes
Answer: Support Vector Machine (SVM)
What does ‘precision’ measure in classification tasks?
A. The proportion of true positives out of all positive predictions
B. The ratio of true positives to the sum of true positives and false negatives
C. The ratio of true positives to the sum of true positives and false positives
D. The accuracy of the model
Answer: The ratio of true positives to the sum of true positives and false positives