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Which of the following is a type of ‘unsupervised learning’ algorithm?
A. K-means Clustering
B. Logistic Regression
C. Linear Regression
D. Decision Trees
Answer: K-means Clustering
What does ‘regularization’ help prevent in machine learning models?
A. Overfitting
B. Underfitting
C. Class imbalance
D. Data leakage
Answer: Overfitting
What is ‘support vector machine’ used for?
A. Classification and regression tasks
B. Dimensionality reduction
C. Clustering data points
D. Feature scaling
Answer: Classification and regression tasks
Which algorithm is used for ‘nearest neighbor’ classification?
A. K-Nearest Neighbors (KNN)
B. Principal Component Analysis (PCA)
C. Support Vector Machine (SVM)
D. Decision Trees
Answer: K-Nearest Neighbors (KNN)
Which of the following techniques is used for ‘feature selection’?
A. Recursive Feature Elimination
B. Principal Component Analysis (PCA)
C. K-means Clustering
D. Support Vector Machine
Answer: Recursive Feature Elimination
What does ‘bias-variance tradeoff’ refer to?
A. The balance between a model's complexity and its performance on training and test data
B. The tradeoff between feature scaling and feature selection
C. The tradeoff between training time and model accuracy
D. The balance between dimensionality reduction and model accuracy
Answer: The balance between a model's complexity and its performance on training and test data
What is ‘bagging’ in ensemble methods?
A. Bootstrap Aggregating
B. Binary Aggregating
C. Batch Aggregating
D. Bayesian Aggregating
Answer: Bootstrap Aggregating
Which of the following is an example of a ‘regression’ algorithm?
A. Linear Regression
B. K-means Clustering
C. Principal Component Analysis
D. Naive Bayes
Answer: Linear Regression
What is the role of ‘hyperparameters’ in machine learning?
A. They are parameters set before training a model to control the training process
B. They are parameters learned during the training process
C. They are used to scale features to a common range
D. They are used to handle missing values in the dataset
Answer: They are parameters set before training a model to control the training process