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What is the main goal of ‘unsupervised learning’?
A. To identify hidden patterns or structures in data without labeled responses
B. To predict outcomes based on labeled data
C. To optimize the parameters of a model
D. To classify data into predefined categories
Answer: To identify hidden patterns or structures in data without labeled responses
Which algorithm is used for ‘supervised learning’?
A. Linear Regression
B. K-means Clustering
C. Principal Component Analysis (PCA)
D. Naive Bayes
Answer: Linear Regression
What is the purpose of ‘data augmentation’ in machine learning?
A. To increase the size of the training dataset by creating modified versions of the original data
B. To reduce the number of features in the dataset
C. To scale the features of the dataset
D. To handle missing values in the data
Answer: To increase the size of the training dataset by creating modified versions of the original data
Which of the following is a common ‘evaluation metric’ for regression models?
A. Mean Squared Error (MSE)
B. F1 Score
C. AUC-ROC
D. Silhouette Score
Answer: Mean Squared Error (MSE)
What does ‘feature extraction’ involve?
A. Creating new features from raw data to improve model performance
B. Selecting a subset of existing features for a model
C. Scaling the features of a dataset
D. Reducing the dimensionality of the data
Answer: Creating new features from raw data to improve model performance
What is ‘loss function’ in machine learning?
A. A function that measures how well a model's predictions match the actual results
B. A function that optimizes the model's parameters
C. A function used to scale the features
D. A function that selects the best model for the dataset
Answer: A function that measures how well a model's predictions match the actual results
What is ‘cross-validation’ used for in model evaluation?
A. Assessing how the results of a statistical analysis will generalize to an independent data set
B. Optimizing the model's hyperparameters
C. Selecting the best features for the model
D. Reducing the size of the dataset
Answer: Assessing how the results of a statistical analysis will generalize to an independent data set
What does ‘ensemble learning’ involve?
A. Combining the predictions of multiple models to improve overall performance
B. Selecting the best features for a model
C. Optimizing the hyperparameters of a model
D. Reducing the dimensionality of the data
Answer: Combining the predictions of multiple models to improve overall performance
Which technique is used to handle ‘missing values’ in a dataset?
A. Imputation
B. Normalization
C. Standardization
D. Feature Selection
Answer: Imputation