Artificial Intelligence and Machine Learning MCQs | STS IBA FPSC BPSC SPSC PPSC Mcqs Test Preparation
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What is ‘decision tree’ used for in machine learning?
A. Classification and Regression
B. Dimensionality Reduction
C. Clustering
D. Feature Extraction
Answer: Classification and Regression
What is ‘ensemble learning’?
A. Combining multiple models to improve performance
B. A technique for dimensionality reduction
C. A method for feature selection
D. A type of unsupervised learning algorithm
Answer: Combining multiple models to improve performance
What is the primary purpose of ‘cross-validation’?
A. To assess the generalizability of a model
B. To reduce the dataset size
C. To select features for the model
D. To tune hyperparameters
Answer: To assess the generalizability of a model
Which of the following metrics is commonly used for evaluating classification models?
A. F1 Score
B. Mean Squared Error (MSE)
C. R-squared
D. Silhouette Score
Answer: F1 Score
Which of the following is NOT a type of neural network architecture?
A. Convolutional Neural Network (CNN)
B. Recurrent Neural Network (RNN)
C. Generative Adversarial Network (GAN)
D. Naive Bayes Network
Answer: Naive Bayes Network
What does the term ‘accuracy’ measure in a classification model?
A. The proportion of correctly classified instances
B. The proportion of false positives
C. The proportion of false negatives
D. The proportion of true negatives
Answer: The proportion of correctly classified instances
Which technique is commonly used to avoid overfitting in machine learning models?
A. Cross-validation
B. Data Augmentation
C. Normalization
D. Feature Selection
Answer: Cross-validation
What is ‘logistic regression’ primarily used for?
A. Classification tasks
B. Regression tasks
C. Clustering tasks
D. Dimensionality reduction
Answer: Classification tasks
What does ‘data augmentation’ do in machine learning?
A. Increases the size of the dataset by creating modified versions of existing data
B. Reduces the dimensionality of the dataset
C. Selects the most important features for the model
D. Optimizes the model's hyperparameters
Answer: Increases the size of the dataset by creating modified versions of existing data