Machine Learning Basics: Test Your Knowledge on Algorithms and Applications
Questions: 30
Questions
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1. Which of the following is a supervised learning algorithm?
- a) Linear regression
- b) K-means clustering
- c) Principal component analysis
- d) K-nearest neighbors
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2. What does the "training set" in machine learning refer to?
- a) The data used to evaluate the model's performance
- b) The data used to train the model and make predictions
- c) The data used to test the algorithm's ability to generalize
- d) The data used to fine-tune the model after training
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3. Which algorithm is used in "unsupervised learning"?
- a) K-means clustering
- b) Logistic regression
- c) Naive Bayes
- d) Linear regression
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4. Which of the following is NOT a type of machine learning algorithm?
- a) Reinforcement learning
- b) Supervised learning
- c) Unsupervised learning
- d) Predictive learning
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5. What is the primary goal of a classification algorithm in machine learning?
- a) To predict a continuous value
- b) To categorize data into distinct classes
- c) To group similar data points into clusters
- d) To reduce the dimensions of data
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6. Which of the following is an example of a regression algorithm?
- a) Linear regression
- b) Decision tree
- c) K-nearest neighbors
- d) Support vector machine
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7. What does the "bias" term in a machine learning model refer to?
- a) The error between predicted and actual values
- b) A model's tendency to learn a simple hypothesis
- c) A constant value that helps the model make predictions
- d) The total amount of data available for training
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8. What does "feature engineering" refer to in machine learning?
- a) Choosing the right algorithm for the problem
- b) Creating new features from the existing data to improve model performance
- c) Tuning the model's hyperparameters
- d) Evaluating the model's accuracy
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9. Which of the following is true about "k-fold cross-validation"?
- a) It splits the dataset into 'k' subsets and trains the model on each subset
- b) It is used to measure the accuracy of a classification model
- c) It always splits the data in half for training and testing
- d) It increases the computational cost of training the model
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10. What does "gradient descent" help optimize in machine learning algorithms?
- a) The features used in the model
- b) The hyperparameters of the model
- c) The model's weights or coefficients
- d) The amount of data used for training
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11. Which of the following algorithms is commonly used for clustering?
- a) K-means clustering
- b) Linear regression
- c) Support vector machine
- d) Naive Bayes
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12. Which algorithm is commonly used for classification tasks?
- a) Logistic regression
- b) K-means clustering
- c) Principal component analysis
- d) Support vector regression
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13. What is the "support vector" in a Support Vector Machine (SVM)?
- a) The points that represent the training set
- b) The points that define the boundary of the data's feature space
- c) The points that help to measure the model's accuracy
- d) The data points that are closest to the hyperplane
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14. What does "ensemble learning" involve?
- a) Combining multiple models to improve prediction accuracy
- b) Using a single model to predict both classification and regression tasks
- c) Training models on different subsets of the data
- d) Reducing the number of features in the dataset
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15. What is the function of the "kernel trick" in a Support Vector Machine?
- a) To transform data into higher dimensions to find a better separating hyperplane
- b) To create more complex decision boundaries for better classification
- c) To normalize the features for better model performance
- d) To split the data into two classes
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16. What does "dimensionality reduction" refer to in machine learning?
- a) Modifying the learning rate of the model
- b) Increasing the number of data points for training
- c) Reducing the number of features in the dataset to improve model efficiency
- d) Selecting the best algorithm for training
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17. Which of the following is an advantage of using decision trees?
- a) They are easy to interpret and visualize
- b) They require a lot of data preprocessing
- c) They perform well with unstructured data
- d) They are computationally expensive
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18. What does the term "feature selection" refer to in machine learning?
- a) Selecting the right algorithm for the problem
- b) Identifying the most relevant features in the data to improve model accuracy
- c) Reducing the amount of data used for training
- d) Dividing the dataset into training and test sets
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19. What is the main objective of supervised learning?
- a) To learn from labeled data and make predictions on unseen data
- b) To group similar data points without labels
- c) To create a model that evolves through interaction with its environment
- d) To reduce the dimensions of the dataset
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20. Which machine learning algorithm is used for classification tasks?
- a) K-means clustering
- b) Logistic regression
- c) Principal component analysis
- d) Linear regression
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21. In which situation would you use a regression algorithm?
- a) When you need to classify objects into categories
- b) When predicting a continuous numerical value
- c) When reducing the dimensionality of data
- d) When grouping similar data points into clusters
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22. Which of the following methods is used to evaluate a classification model's performance?
- a) Mean squared error
- b) R-squared
- c) Confusion matrix
- d) F1-score
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23. What does "feature scaling" help achieve in machine learning?
- a) It reduces the number of features used by the model
- b) It ensures that all features have the same range of values
- c) It creates more features to improve model performance
- d) It helps split the data into training and test sets
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24. What does "bagging" stand for in ensemble methods?
- a) Bootstrapped aggregation
- b) Balanced aggregation
- c) Binned aggregation
- d) Basic aggregation
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25. Which of the following algorithms is used for dimensionality reduction?
- a) K-means clustering
- b) Support vector machines
- c) Principal component analysis
- d) Logistic regression
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26. What is the purpose of regularization in machine learning?
- a) To reduce overfitting by penalizing large model coefficients
- b) To increase the complexity of the model
- c) To enhance the model's ability to generalize to unseen data
- d) To speed up the training process
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27. What does the "learning rate" control in a machine learning model?
- a) The number of iterations the model runs
- b) The size of the data batches used in training
- c) How quickly the model adjusts to the loss function
- d) The number of features to include in the model
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28. Which of the following is true about Support Vector Machines (SVM)?
- a) They are used for classification and regression tasks
- b) They can only perform classification tasks
- c) They do not work well with non-linear data
- d) They require less computational power compared to other algorithms
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29. What does "deep learning" refer to in machine learning?
- a) Using complex neural networks with multiple layers for learning
- b) A method to reduce the size of the dataset
- c) A classification technique for small datasets
- d) A supervised learning technique with few parameters
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30. Which technique can be used to prevent overfitting in neural networks?
- a) Cross-validation
- b) Regularization
- c) Grid search
- d) Feature scaling
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