Artificial Intelligence and Machine Learning MCQs | STS IBA FPSC BPSC SPSC PPSC Mcqs Test Preparation
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Which technique is used to handle class imbalance in a dataset?
A. Resampling
B. Normalization
C. Feature Selection
D. Dimensionality Reduction
Answer: Resampling
What does ‘hyperparameter tuning’ involve?
A. Adjusting the parameters of a machine learning model to improve performance
B. Choosing the features for the model
C. Scaling the data for the model
D. Splitting the dataset into training and testing sets
Answer: Adjusting the parameters of a machine learning model to improve performance
Which of the following is NOT a type of neural network?
A. Generative Adversarial Network (GAN)
B. Convolutional Neural Network (CNN)
C. Recurrent Neural Network (RNN)
D. K-means Neural Network
Answer: K-means Neural Network
What is ‘support vector machine’ (SVM) used for?
A. Classification and Regression
B. Clustering
C. Dimensionality Reduction
D. Data Augmentation
Answer: Classification and Regression
Which of the following is an example of an ensemble learning method?
A. Random Forest
B. Support Vector Machine (SVM)
C. Principal Component Analysis (PCA)
D. K-nearest Neighbors (KNN)
Answer: Random Forest
What type of algorithm is the ‘K-means’ algorithm?
A. Clustering Algorithm
B. Classification Algorithm
C. Regression Algorithm
D. Dimensionality Reduction Algorithm
Answer: Clustering Algorithm
Which of the following is a metric for evaluating regression models?
A. Mean Squared Error (MSE)
B. Accuracy
C. F1 Score
D. Precision
Answer: Mean Squared Error (MSE)
What does ‘dropout’ do in neural networks?
A. Reduces overfitting by randomly setting a fraction of input units to 0
B. Increases the number of neurons in the network
C. Improves the learning rate
D. Simplifies the model architecture
Answer: Reduces overfitting by randomly setting a fraction of input units to 0
What is ‘feature engineering’?
A. The process of using domain knowledge to create features that make machine learning algorithms work better
B. The process of selecting the best model for a dataset
C. The process of optimizing the model's hyperparameters
D. The process of normalizing the dataset
Answer: The process of using domain knowledge to create features that make machine learning algorithms work better