Machine Learning MCQs – Page 5

#1. Which technique is used to reduce the risk of overfitting in decision trees?

Pruning

Grafting

Bagging

Boosting

Random selection of features

#2. What does the term “bias-variance tradeoff” refer to in machine learning?

The balance between model complexity and the amount of training data

The balance between underfitting and overfitting

The tradeoff between the accuracy of a model and its interpretability

The tradeoff between bias (error due to too simple model) and variance (error due to too complex model)

The tradeoff between the number of features and the number of samples in the dataset

#3. What is the purpose of a confusion matrix in classification problems?

To visualize the performance of a model

To display the distribution of classes in a dataset

To evaluate the quality of features

To quantify the performance of a classification model

To calculate the probability of class membership

#4. Which algorithm is commonly used for collaborative filtering in recommendation systems?

Matrix Factorization

K-Nearest Neighbors (KNN)

Support Vector Machines (SVM)

Decision Trees

Random Forest

#5. What is the role of a learning rate in gradient descent optimization?

It controls the size of the steps taken during optimization

It determines the number of training samples

It specifies the number of features in the dataset

It defines the number of iterations in training

It adjusts the complexity of the model

#6. In a neural network, what is the purpose of the activation function?

To introduce non-linearity into the model

To reduce the number of parameters

To improve the interpretability of the model

To speed up the training process

To add noise to the data

#7. Which type of machine learning algorithm is well-suited for recommendation systems?

Collaborative Filtering

Decision Trees

Support Vector Machines (SVM)

K-Means Clustering

Linear Regression

#8. What is the objective of Principal Component Analysis (PCA) in dimensionality reduction?

To project data onto a lower-dimensional subspace while retaining the most important information

To increase the number of features in the dataset

To separate data into distinct clusters

To minimize the training time

To transform data into a non-linear space

#9. What is the main advantage of using an ensemble learning method like Random Forest?

It reduces overfitting and increases model robustness

It simplifies the model and improves interpretability

It speeds up the training process

It eliminates the need for feature engineering

It requires fewer training samples

#10. In which phase of a machine learning project is cross-validation typically applied?

Model evaluation

Data preprocessing

Model training

Model deployment

Data collection

#11. What is the purpose of L1 regularization (Lasso) in linear regression?

It encourages sparsity by penalizing the absolute values of coefficients

It increases the number of features in the model

It adds noise to the data

It reduces the complexity of the model

It enforces non-negativity of coefficients

#12. Which algorithm is used for finding frequent itemsets in association rule mining?

Apriori algorithm

K-Means Clustering

Naive Bayes

Decision Trees

Support Vector Machines (SVM)

#13. What is the objective of batch normalization in deep learning?

To stabilize and speed up the training process

To reduce the number of parameters in the model

To increase the learning rate

To add noise to the data

To simplify the model

#14. Which technique is used to combat the class imbalance problem in classification tasks?

Resampling (e.g., oversampling or undersampling)

Bagging

Pruning

Feature selection

Dimensionality reduction

#15. What is the purpose 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

#16. Which algorithm is commonly used for text classification in natural language processing (NLP)?

Support Vector Machines (SVM)

Random Forest

Logistic Regression

Naive Bayes

Decision Trees

#17. What is the purpose of the term “dropout” in deep learning?

To randomly deactivate neurons during training to prevent overfitting

To increase the learning rate

To add noise to the data

To reduce model complexity

To improve the interpretability of the model

#18. What is the primary objective of the Expectation-Maximization (EM) algorithm in unsupervised learning?

Estimating the parameters of a probabilistic model in the presence of hidden variables

Reducing model complexity

Performing feature selection

Adding non-linearity to the model

Performing clustering

#19. Which technique is used for feature importance ranking in Random Forest models?

Gini Impurity or Mean Decrease in Gini

Recursive Feature Elimination (RFE)

Principal Component Analysis (PCA)

Support Vector Machines (SVM)

Singular Value Decomposition (SVD)

#20. What is the purpose of the term “regularization” in machine learning?

To prevent overfitting by adding a penalty term to the loss function

To increase the number of features in the model

To reduce the learning rate

To control the number of samples in the training set

To add noise to the data

Finish

Results

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