🎯 Test Your Knowledge!​

1. Which algorithm aims to find the best-fitting line through data points to predict a continuous output variable?

2. An autonomous robot learns to navigate a complex environment by receiving rewards for desired actions and penalties for undesirable ones. What machine learning paradigm is demonstrated here?

3. Which metric is used to evaluate a classification model's performance by considering both precision and recall, especially with imbalanced datasets?

4. A model consistently predicts higher values than the actual ones. This indicates a problem primarily related to which aspect of the model?

5. To compare different classification models, a data scientist plots the True Positive Rate against the False Positive Rate at various threshold settings. What curve are they generating?

6. A data scientist is building a model to predict house prices based on features like size, location, and number of bedrooms. What type of machine learning problem are they solving?

7. A company wants to build a recommendation system where users who liked certain products are suggested similar items. Which type of machine learning algorithm would be most suitable for this task?

8. What is a "feature" in the context of machine learning?

9. What technique is used to divide a dataset into multiple subsets, training the model on some and validating it on others, to assess its performance reliably?

10. Which algorithm constructs a tree-like model of decisions and their possible consequences, often used for both classification and regression?

11. What is the process of converting raw data into a suitable format for a machine learning model?

12. You are trying to group similar customers together based on their purchasing behavior without any pre-defined categories. Which machine learning task would you use?

13. An email service uses an algorithm to categorize incoming emails as "spam" or "not spam" based on their content and sender. What type of supervised learning problem is this?

14. Which machine learning paradigm involves training a model on labeled data, where inputs are mapped to known outputs?

15. Which of the following is a common issue where a model performs poorly on new data because it has learned the training data too specifically, including noise?