Artificial Intelligence and Machine Learning MCQs | STS IBA FPSC BPSC SPSC PPSC Mcqs Test Preparation

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What is ‘logistic regression’ used for?

A. Binary classification tasks
B. Clustering data points into groups
C. Dimensionality reduction
D. Optimization of model parameters

Answer: Binary classification tasks

What does ‘feature scaling’ involve?

A. Transforming features to a common scale to improve model performance
B. Selecting the best features for the model
C. Reducing the dimensionality of the data
D. Handling missing values in the dataset

Answer: Transforming features to a common scale to improve model performance

What is ‘transfer learning’?

A. A technique where a model trained on one task is used as a starting point for a different but related task
B. A method for dimensionality reduction
C. A technique for feature selection
D. A way to handle missing values in the dataset

Answer: A technique where a model trained on one task is used as a starting point for a different but related task

What does ‘dimensionality reduction’ involve?

A. Reducing the number of features or variables in the dataset while retaining important information
B. Scaling the features of the dataset
C. Selecting the best features for the model
D. Clustering similar data points

Answer: Reducing the number of features or variables in the dataset while retaining important information

Which of the following algorithms is used for ‘feature selection’?

A. Recursive Feature Elimination (RFE)
B. K-means Clustering
C. Naive Bayes
D. Principal Component Analysis (PCA)

Answer: Recursive Feature Elimination (RFE)

What is the role of ‘gradient descent’ in machine learning?

A. To optimize the parameters of a model by minimizing the cost function
B. To scale the features of the dataset
C. To reduce the dimensionality of the data
D. To evaluate the performance of a model

Answer: To optimize the parameters of a model by minimizing the cost function

What does ‘regularization’ do in machine learning?

A. It adds a penalty to the model's complexity to prevent overfitting
B. It scales the features to a common range
C. It reduces the number of features in the dataset
D. It helps in feature selection

Answer: It adds a penalty to the model's complexity to prevent overfitting

What is the purpose of ‘cross-validation’?

A. To assess how well a model generalizes to an independent dataset
B. To select the best features for the model
C. To reduce the dimensionality of the data
D. To handle missing values in the dataset

Answer: To assess how well a model generalizes to an independent dataset

What is ‘boosting’ in ensemble learning?

A. A technique that combines weak learners to create a strong learner by giving more weight to misclassified examples
B. A method to combine multiple models to improve performance
C. A technique for dimensionality reduction
D. A way to select the best features for a model

Answer: A technique that combines weak learners to create a strong learner by giving more weight to misclassified examples