Welcome to your Uncovering the Top Machine Learning Quizzes 2024 (Part 4) This quiz has multiple choice questions and each has only one correct answer.
Total Questions : 24
To pass this quiz you need to score 80% or above.
Time Provided : 45 minutes
Today's Date : 28 May 2025
All the best!!
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1. Which machine learning method is used when there are no labeled responses?
2. What is an application of supervised learning?
3. Which algorithm can be used for clustering in unsupervised learning?
4. What term refers to the parameters in a machine learning model that are adjusted during the training process to minimize a specified cost function?
5. Which technique involves removing data points that fall outside a specified range of values?
6. In which type of machine learning does the model learn from labeled data to make predictions or decisions?
7. What is the primary objective of unsupervised learning?
8. In which of the following ML methods, the model learns by interacting with an environment and receiving feedback in the form of rewards or penalties?
9. What does the parameter 'axis' in the dropna() method represent?
10. In Python, which function is used for Z-score normalization?
11. Which machine learning method is commonly used in natural language processing (NLP) tasks such as sentiment analysis?
12. What method can be used to handle missing data by replacing missing values with the mean of the column?
13. What is a technique used for feature scaling that transforms the data into a range between 0 and 1?
14. What method in scikit-learn is commonly used for outlier detection based on the interquartile range?
15. What is the purpose of hyperparameter tuning in machine learning?
16. In pandas, which method is used to drop rows with missing values from a DataFrame?
17. What is a technique used for feature scaling that transforms the data to have zero mean and unit variance?
18. Which of the following is an example of supervised learning algorithm?
19. Which Python library offers the 'SimpleImputer' class for handling missing data?
20. What is feature extraction in feature engineering?
21. How does cross-validation help in hyperparameter tuning?
22. What is an example of a real-world application of reinforcement learning?
23. What is grid search in the context of hyperparameter tuning?
24. Which function is commonly used to measure the difference between the predicted values of a model and the actual values in supervised learning?
