Question : 1 Which of the following is NOT a type of machine learning?
Supervised Learning
Unsupervised Learning
Biased Learning
Reinforcement Learning
Question : 2 What is the objective of regression analysis in machine learning?
Classification
Clustering
Predicting continuous values
Finding patterns
Question : 3 Which algorithm is commonly used for classification problems in machine learning?
K-Means
K-Nearest Neighbors
Decision Trees
Linear Regression
Question : 4 What is the main goal of unsupervised learning?
Predicting outcomes
Making decisions
Discovering patterns and relationships
Optimizing a function
Question : 5 Which evaluation metric is commonly used for classification problems?
Mean Squared Error
Accuracy
Root Mean Squared Error
R² Score
Question : 6 Which technique is used to handle missing data in machine learning?
Mean Imputation
Median Imputation
Mode Imputation
All of the above
Question : 7 What is the primary purpose of feature scaling in machine learning?
To increase the dimensionality of features
To reduce overfitting
To speed up training
To normalize the range of features
Question : 8 Which algorithm is commonly used for anomaly detection in machine learning?
K-Means
Decision Trees
Isolation Forest
Linear Regression
Question : 9 Which technique is used to reduce the dimensionality of data in machine learning?
Feature Engineering
Principal Component Analysis (PCA)
Cross-Validation
Gradient Descent
Question : 10 What is the main advantage of using ensemble learning methods?
They are simple to implement
They always provide accurate predictions
They reduce overfitting and increase accuracy
They require less computational resources
Question : 11 What is the purpose of cross-validation in machine learning?
To split the dataset into training and testing sets
To select the best hyperparameters
To evaluate model performance and prevent overfitting
To train the model on multiple datasets
Question : 12 Which algorithm is commonly used for regression problems in machine learning?
K-Means
Linear Regression
Decision Trees
K-Nearest Neighbors
Question : 13 What is the primary goal of model evaluation in machine learning?
To memorize the training data
To generalize well to unseen data
To overfit the training data
To increase model complexity
Question : 14 Which technique is used to handle imbalanced datasets in machine learning?
Feature Scaling
Overfitting
Resampling
Regularization
Question : 15 What is the purpose of hyperparameter tuning in machine learning?
To preprocess the data
To select the best features
To optimize model performance by selecting the best hyperparameters
To train the model on multiple datasets
Question : 16 What is the primary goal of regularization in machine learning?
To increase model complexity
To reduce model complexity and prevent overfitting
To memorize the training data
To improve computational efficiency
Question : 17 Which technique is used to handle categorical variables in machine learning?
Feature Scaling
One-Hot Encoding
Standardization
Imputation
Question : 18 What is the main purpose of a validation set in machine learning?
To train the model
To tune hyperparameters and evaluate model performance
To test the model on unseen data
To preprocess the data
Question : 19 Which evaluation metric is commonly used for regression problems in machine learning?
Accuracy
Precision
Mean Squared Error
Recall
Question : 20 What is the purpose of a confusion matrix in machine learning?
To visualize the decision boundary of the model
To evaluate the performance of a classification model
To handle missing data
To optimize hyperparameters
Question : 21 Which algorithm is commonly used for text classification tasks in machine learning?
K-Means
Naive Bayes
Random Forest
Support Vector Machine
Question : 22 What is the main objective of gradient descent optimization in machine learning?
To maximize the likelihood function
To minimize the cost function by adjusting model parameters
To prevent overfitting
To calculate feature importance
Question : 23 Which technique is used to handle overfitting in machine learning?
Feature Engineering
Regularization
Cross-Validation
Ensemble Learning
Question : 24 What is the main objective of cross-entropy loss function in machine learning?
To minimize the difference between predicted and actual values
To measure the uncertainty in predictions
To maximize the likelihood function
To regularize the model
Question : 25 Which algorithm is commonly used for recommendation systems in machine learning?
K-Means
K-Nearest Neighbors
Matrix Factorization
Decision Trees