Title: Quiz 11 — Machine Learning Libraries (scikit-learn)
Course: AIINFRA 100: AI Systems & Development Environments > Section: Week 11 — Machine Learning Libraries: scikit-learn

Q: Which two methods does every scikit-learn estimator implement as part of its uniform API?
- `train()` and `test()`
- `fit()` and `predict()`
- `load()` and `save()`
- `compile()` and `run()`
A: `fit()` and `predict()`

Q: scikit-learn's models, such as decision trees and k-nearest neighbors, are neural networks.
- True
- False
A: False

Q: What is the correct order of the typical scikit-learn workflow?
Feedback: Sample/expected answer: Split → Load → Predict → Evaluate

Q: Evaluating a model on the same data it was trained on gives an accurate picture of how it will perform on new, unseen data.
- True
- False
A: False

Q: What term describes a model that performs very well on training data but poorly on new/unseen data?
Feedback: Sample/expected answer: Overfitting

Q: Which scikit-learn tool lets you get a more reliable performance estimate by training and testing across multiple folds of the data instead of a single split?
- `train_test_split`
- `Pipeline`
- `cross_val_score` / `cross_validate`
- `load_iris`
A: `cross_val_score` / `cross_validate`

Q: Which pair of metrics would you use to evaluate a regression model (predicting a continuous number)?
- Accuracy and F1
- Precision and Recall
- MAE and R²
- Confusion matrix and Accuracy
A: MAE and R²

Q: What is the name of the scikit-learn class that bundles preprocessing steps (like scaling) together with a model, so that training and inference stay consistent and test data doesn't leak into training?
Feedback: Sample/expected answer: Pipeline (sklearn.pipeline.Pipeline)