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What does ‘feature importance’ indicate?
A. The relative significance of each feature in predicting the target variable
B. The need to scale the features to a common range
C. The necessity of dimensionality reduction
D. The process of handling missing values
Answer: The relative significance of each feature in predicting the target variable
Which technique is used to ‘reduce overfitting’ in machine learning models?
A. Regularization
B. Feature Scaling
C. Data Augmentation
D. Dimensionality Reduction
Answer: Regularization
What does ‘outlier detection’ involve?
A. Identifying and handling data points that deviate significantly from the majority of the data
B. Scaling the features of the dataset
C. Selecting the best features for the model
D. Reducing the dimensionality of the data
Answer: Identifying and handling data points that deviate significantly from the majority of the data
What is ‘ensemble method’?
A. Combining predictions from multiple models to improve overall performance
B. A method for dimensionality reduction
C. A technique for feature scaling
D. A way to handle missing values in the dataset
Answer: Combining predictions from multiple models to improve overall performance
Which of the following is a technique for ‘handling missing values’?
A. Imputation
B. Dimensionality Reduction
C. Feature Scaling
D. Clustering
Answer: Imputation
What is ‘cross-entropy loss’ used for?
A. Evaluating the performance of classification models
B. Optimizing regression models
C. Reducing the dimensionality of the data
D. Scaling features to a common range
Answer: Evaluating the performance of classification models
What is ‘model selection’ in machine learning?
A. Choosing the best model from a set of candidate models based on performance metrics
B. Selecting the most relevant features from the dataset
C. Scaling the features to a common range
D. Handling missing values in the dataset
Answer: Choosing the best model from a set of candidate models based on performance metrics
Which of the following metrics is used to evaluate the performance of a regression model?
A. R-squared (R²)
B. Confusion Matrix
C. Silhouette Score
D. F1 Score
Answer: R-squared (R²)
What does ‘support vector machine’ (SVM) do?
A. Classifies data by finding the hyperplane that best separates different classes
B. Clusters similar data points into groups
C. Reduces the dimensionality of the data
D. Handles missing values in the dataset
Answer: Classifies data by finding the hyperplane that best separates different classes