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What is ‘ensemble averaging’?
A. Combining predictions from multiple models by averaging their outputs
B. Scaling features to a common range
C. Reducing the number of features in the dataset
D. Handling missing values in the dataset
Answer: Combining predictions from multiple models by averaging their outputs
Which of the following is a ‘hyperparameter’?
A. Learning Rate
B. Precision
C. Recall
D. F1 Score
Answer: Learning Rate
What is ‘dropout’ in neural networks?
A. A technique used to prevent overfitting by randomly dropping units during training
B. A method for feature scaling
C. A technique for dimensionality reduction
D. A type of activation function
Answer: A technique used to prevent overfitting by randomly dropping units during training
Which of the following is an example of a ‘dimensionality reduction’ technique?
A. Principal Component Analysis (PCA)
B. K-Nearest Neighbors (KNN)
C. Decision Trees
D. Naive Bayes
Answer: Principal Component Analysis (PCA)
What is ‘early stopping’?
A. A technique to prevent overfitting by halting training when performance on a validation set starts to degrade
B. A method to scale features to a common range
C. A technique for feature selection
D. A method for dimensionality reduction
Answer: A technique to prevent overfitting by halting training when performance on a validation set starts to degrade
What is ‘grid search’ used for?
A. Finding the best hyperparameters for a machine learning model
B. Scaling features to a common range
C. Reducing the number of features in the dataset
D. Dimensionality reduction
Answer: Finding the best hyperparameters for a machine learning model
Which of the following is a ‘non-linear’ algorithm?
A. Support Vector Machine (SVM) with a non-linear kernel
B. Linear Regression
C. Principal Component Analysis (PCA)
D. Naive Bayes
Answer: Support Vector Machine (SVM) with a non-linear kernel
Which of the following is a ‘probabilistic’ model?
A. Naive Bayes
B. Decision Trees
C. Support Vector Machine (SVM)
D. Linear Regression
Answer: Naive Bayes
What is ‘batch normalization’ used for?
A. Improving the training speed and stability of deep neural networks
B. Scaling features to a common range
C. Reducing the dimensionality of the data
D. Feature extraction
Answer: Improving the training speed and stability of deep neural networks