By default, a
[`BaggingClassifier`](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.BaggingClassifier.html)
or [`BaggingRegressor`](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.BaggingRegressor.html)
draw:
- a) random samples with replacement over training points
- b) random samples with replacement over features
- c) random samples without replacement over training points
- d) random samples without replacement over features
Hint: it is possible to access the documentation for those classes by
clicking on the links on their name.
+++
In a
[`BaggingClassifier`](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.BaggingClassifier.html)
or [`BaggingRegressor`](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.BaggingRegressor.html),
the parameter `base_estimator` is:
- a) any predictor
- b) a decision tree predictor
- c) a linear model predictor
+++
In the context of a classification problem, what are the differences between a
bagging classifier and a random forest classifier:
- a) in a random forest, the base model is always a decision tree
- b) in a random forest, the split threshold values are decided completely at
random
- c) in a random forest, a random resampling is performed both over features
as well as over samples