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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is data preparation and why is it important?
💡 Hint: Think about the impact of quality data on outcomes.
Question 2
Easy
Name one common dataset used for classification tasks.
💡 Hint: Consider datasets that are often cited in machine learning examples.
Practice 4 more questions and get performance evaluation
Interactive Quizzes
Engage in quick quizzes to reinforce what you've learned and check your comprehension.
Question 1
What is the main purpose of the train-test split?
- To balance the classes
- To evaluate model performance on unseen data
- To make the model more complex
💡 Hint: Think about the importance of data you've held back from training.
Question 2
True or False: Scaling numerical features is optional in data preprocessing.
- True
- False
💡 Hint: Consider the impact of different feature ranges.
Solve and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Imagine you have a dataset containing various fruits with features like weight, color, and sugar content. Design a preprocessing strategy for this dataset, considering any necessary transformations.
💡 Hint: Consider ways the data might need cleaning or unifying.
Question 2
You find that the dataset you are working with has significantly imbalanced classes. Propose a solution to preprocess and prepare this data for training a classification algorithm.
💡 Hint: Think about how to make classes more even before model training.
Challenge and get performance evaluation