Practice Train/Test Split - 3 | Classification Algorithms | Data Science Basic
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3 - Train/Test Split

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Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is the purpose of the Train/Test Split?

💡 Hint: Think about why testing on unseen data is necessary.

Question 2

Easy

What can you infer if your model performs well on the training set but poorly on the test set?

💡 Hint: Consider model bias and prediction generalization.

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 primary goal of a Train/Test Split?

  • A) To increase the dataset size
  • B) To create a validation set
  • C) To evaluate model performance on unseen data
  • D) To reduce the training time

💡 Hint: Consider what the primary function of separating data into two parts is.

Question 2

True or False: The 'random_state' parameter ensures reproducibility of the Train/Test Split.

  • True
  • False

💡 Hint: Think about why reproducing results are important in data science.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Consider a dataset of 10,000 samples. If you want to perform an 80/20 train/test split, how many samples would be used for training and testing?

💡 Hint: Calculate 20% of the total samples for testing.

Question 2

In a scenario where your model performs excellently on the training data yet poorly on test data, what potential issues could cause this discrepancy? Discuss and suggest solutions.

💡 Hint: Think about the relationship between training data, model complexity, and generalization.

Challenge and get performance evaluation