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