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