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