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What is ‘latent variable’ in machine learning?
A. A variable that is not directly observed but is inferred from the model
B. A variable that is used to reduce the number of features
C. A variable that indicates the accuracy of the model
D. A variable used to scale the data
Answer: A variable that is not directly observed but is inferred from the model
Which algorithm is used for ‘dimensionality reduction’ in large datasets?
A. Principal Component Analysis (PCA)
B. Random Forest
C. K-nearest Neighbors (KNN)
D. Naive Bayes
Answer: Principal Component Analysis (PCA)
What is ‘feature scaling’ used for?
A. Standardizing the range of features
B. Selecting the most important features
C. Reducing the dimensionality of the data
D. Optimizing the model's hyperparameters
Answer: Standardizing the range of features
Which of the following is an example of an unsupervised learning algorithm?
A. K-means Clustering
B. Logistic Regression
C. Linear Regression
D. Naive Bayes
Answer: K-means Clustering
Which algorithm is used for ‘feature selection’?
A. Recursive Feature Elimination (RFE)
B. K-means Clustering
C. Principal Component Analysis (PCA)
D. Support Vector Machine (SVM)
Answer: Recursive Feature Elimination (RFE)
What does ‘dropout’ help with in neural networks?
A. Preventing overfitting
B. Increasing the training speed
C. Improving feature selection
D. Reducing the number of features
Answer: Preventing overfitting
What is ‘recurrent neural network’ (RNN) used for?
A. Processing sequential data
B. Dimensionality reduction
C. Classification of images
D. Clustering data points
Answer: Processing sequential data
What does ‘Support Vector Machine’ (SVM) aim to achieve?
A. Finding the optimal hyperplane that separates different classes
B. Reducing the dimensionality of the data
C. Clustering data into distinct groups
D. Performing regression analysis
Answer: Finding the optimal hyperplane that separates different classes
What is ‘regularization’ used for in machine learning?
A. To prevent overfitting by adding a penalty to the model complexity
B. To enhance the training speed of the model
C. To increase the number of features in the dataset
D. To perform cross-validation
Answer: To prevent overfitting by adding a penalty to the model complexity