Machine Learning MCQs

#1. Which method is commonly used for hyperparameter tuning in machine learning?

Grid Search

Random Search

Gradient Descent

K-Nearest Neighbors (KNN)

Apriori algorithm

#2. What is the purpose of a one-hot encoding in preprocessing categorical data?

Representing categorical variables as binary vectors

Reducing the number of features

Speeding up the training process

Increasing the learning rate

Adding noise to the data

#3. Which technique is used for outlier detection in a dataset?

Isolation Forest

K-Means Clustering

PCA

Linear Regression

Logistic Regression

#4. What is the main objective of the Mean-Shift clustering algorithm?

Identifying dense regions of data points in feature space

Reducing model complexity

Performing image segmentation

Classifying data into predefined categories

Increasing the learning rate

#5. What is the purpose of a loss function in machine learning?

Quantifying the error between predicted and actual values

Determining the number of hidden layers in a neural network

Controlling the number of features in a dataset

Adding non-linearity to the model

Speeding up the training process

#6. In reinforcement learning, what is the role of the discount factor (gamma)?

Balancing immediate rewards against future rewards

Controlling the exploration rate

Reducing model complexity

Controlling the learning rate

Controlling the number of episodes in training

#7. Which method is commonly used for imbalanced classification tasks?

Resampling (e.g., oversampling or undersampling)

Bagging

Feature selection

Principal Component Analysis (PCA)

Random Projection

#8. What is the purpose of the Kullback-Leibler (KL) divergence in information theory?

Measuring the difference between two probability distributions

Classifying data into predefined categories

Speeding up the training process

Reducing model complexity

Adding noise to the data

#9. Which algorithm is commonly used for face recognition in computer vision applications?

Eigenfaces (PCA-based)

Random Forest

Logistic Regression

K-Nearest Neighbors (KNN)

Naive Bayes

#10. What is the primary objective of the Viterbi algorithm in sequence labeling tasks?

Finding the most likely sequence of hidden states in a Hidden Markov Model (HMM)

Performing image classification

Reducing model complexity

Adding non-linearity to the model

Improving the interpretability of the model

#11. What is the purpose of the term “batch size” in deep learning?

The number of training samples processed before updating the model's weights

To control the number of features in a dataset

To add noise to the data

To reduce model complexity

To increase the learning rate

#12. Which technique is used for reducing the dimensionality of high-dimensional data while preserving as much information as possible?

t-SNE (t-Distributed Stochastic Neighbor Embedding)

Random Projection

Singular Value Decomposition (SVD)

Apriori algorithm

K-Means Clustering

#13. What is the purpose of the term “bagging” in ensemble learning?

To combine multiple base models with bootstrap sampling to improve performance

To reduce the learning rate

To add noise to the data

To increase the number of features

To improve the interpretability of the model

#14. Which technique is commonly used for feature selection in machine learning?

Recursive Feature Elimination (RFE)

Principal Component Analysis (PCA)

Support Vector Machines (SVM)

K-Means Clustering

Naive Bayes

#15. Which algorithm is used for anomaly detection in machine learning?

Isolation Forest

Logistic Regression

K-Means Clustering

Decision Trees

Support Vector Machines (SVM)

#16. What is the purpose of the bias term in a neural network?

To shift the activation function to the left or right

To reduce overfitting

To increase model complexity

To regularize the model

To add non-linearity

#17. What is the goal of unsupervised learning?

Discovering hidden patterns or structures in data

Maximizing prediction accuracy

Minimizing model complexity

Minimizing training time

Classifying data into predefined categories

#18. Which method is commonly used to handle missing data in a dataset?

Imputation

Deletion

Ignoring it during training

Normalization

Standardization

#19. What is the purpose of the Adam optimizer in deep learning?

A stochastic gradient descent optimization algorithm

A clustering algorithm

A dimensionality reduction technique

A regularization technique

A feature extraction method

#20. In reinforcement learning, what is the agent’s objective?

To maximize the cumulative reward over time

To minimize the cumulative reward over time

To memorize the training data

To predict future states

To classify data into predefined categories

#1. Which method is commonly used for hyperparameter tuning in machine learning?

#2. What is the purpose of a one-hot encoding in preprocessing categorical data?

#3. Which technique is used for outlier detection in a dataset?

#4. What is the main objective of the Mean-Shift clustering algorithm?

#5. What is the purpose of a loss function in machine learning?

#6. In reinforcement learning, what is the role of the discount factor (gamma)?

#7. Which method is commonly used for imbalanced classification tasks?

#8. What is the purpose of the Kullback-Leibler (KL) divergence in information theory?

#9. Which algorithm is commonly used for face recognition in computer vision applications?

#10. What is the primary objective of the Viterbi algorithm in sequence labeling tasks?

#11. What is the purpose of the term “batch size” in deep learning?

#12. Which technique is used for reducing the dimensionality of high-dimensional data while preserving as much information as possible?

#13. What is the purpose of the term “bagging” in ensemble learning?

#14. Which technique is commonly used for feature selection in machine learning?

#15. Which algorithm is used for anomaly detection in machine learning?

#16. What is the purpose of the bias term in a neural network?

#17. What is the goal of unsupervised learning?

#18. Which method is commonly used to handle missing data in a dataset?

#19. What is the purpose of the Adam optimizer in deep learning?

#20. In reinforcement learning, what is the agent’s objective?

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