9.4 - Step 3: Feature Selection and Splitting
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Practice Questions
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What is a label in the context of machine learning?
💡 Hint: Think about what we are trying to find out with our model.
Why do we separate features from labels?
💡 Hint: Remember, we want the model to learn from data without knowing the result initially.
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Interactive Quizzes
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What is the primary purpose of feature selection?
💡 Hint: Think about what can make a machine learning model more effective.
True or False: Splitting the dataset into training and testing sets helps prevent overfitting.
💡 Hint: What happens if you train and test on the same data?
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Challenge Problems
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Imagine you have a dataset with a large number of features. How would you approach the feature selection process for building a model?
💡 Hint: Consider which features have the most significant impact on the outcome.
Given a dataset of 500 instances with both features and labels, explain how you’d choose an appropriate ratio for a train-test split.
💡 Hint: Think about sample size and how to balance training and evaluation.
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