12.5 - Train-Test Split
Enroll to start learning
You’ve not yet enrolled in this course. Please enroll for free to listen to audio lessons, classroom podcasts and take practice test.
Practice Questions
Test your understanding with targeted questions
What are the two main sets in a Train-Test Split?
💡 Hint: Think about what data you need to train the model and what data is needed to evaluate it.
Why do we use only part of the dataset for training?
💡 Hint: Consider what would happen if we used the entire dataset for training.
4 more questions available
Interactive Quizzes
Quick quizzes to reinforce your learning
What does the Train-Test Split technique do in AI?
💡 Hint: Consider what happens when preparing data for an AI model.
True or False: Overfitting occurs when a model performs well on new data.
💡 Hint: Think about the consequences of excessive learning.
2 more questions available
Challenge Problems
Push your limits with advanced challenges
You are given a dataset with 10,000 observations. Design a splitting strategy to ensure that the training and testing datasets are representative of the overall distribution. Discuss your approach and justify your split ratios.
💡 Hint: Think about the diversity and distribution of your dataset.
Evaluate the potential effects of using a very small testing set size (e.g., 5% of the total dataset) on the reliability of your model's evaluation. What are the risks, and how can they be mitigated?
💡 Hint: Consider what would happen if your testing data did not represent the different scenarios in your dataset.
Get performance evaluation
Reference links
Supplementary resources to enhance your learning experience.