- The average of guesses of a large number of people will typically be more accurate than an individual can be
	- (wisdom of crowds)
- Bootstrapping
	- generates simulated samples by sampling with replacement from existing sample
	- sampling is random
	- Out of bag sample
		- data that was never picked in bootstrapping
		- is often used for testing data (OOB samples were not in the training samples so the model has never seen them)
		- with common techniques often ~30% of data is not picked (and forms OOB set)
	- distribution of classes with bootstrapping will form a Gaussian distribution
	- when enough samples are used bootstrap distributions will approximate population statistics
		- Assumption is made with bootstrapping that the sample approximates the original population
	- bootstrap samples are used as training sets, OOB samples serve as testing sets
```python
bootstrap_samples = [resample(df, replace=True, random_state=i) for i in range(5)]
```