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4 cards from this section. Quick, good the night before a test.
What are the two main sets in a Train-Test Split?
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?
Consider what would happen if we used the entire dataset for training.
What does the Train-Test Split technique do in AI?
Consider what happens when preparing data for an AI model.
True or False: Overfitting occurs when a model performs well on new data.
Think about the consequences of excessive learning.
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.
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?
Consider what would happen if your testing data did not represent the different scenarios in your dataset.
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