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What is the Bootstrap method in data science?

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Bootstrapping is a technique used to make estimations from data by taking an average of the estimates from smaller data samples.

Method

The bootstrap method involves iteratively resampling a dataset with replacement. Instead of only estimating our statistic once on the complete data, we can do it many times on a re-sampling (with replacement) of the original sample. Repeating this re-sampling multiple times allows us to obtain a vector of estimates. We can then compute variance, expected value, empirical distribution, and other relevant statistics of these estimates.

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Uses

Bootstrapping allows statistical inferences to be made about the population distribution of a small sample. It is used to account for distortions caused by certain sample data that could make for a bad representation of the overall data.


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python
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Grokking Modern System Design Interview for Engineers & Managers

Ace your System Design Interview and take your career to the next level. Learn to handle the design of applications like Netflix, Quora, Facebook, Uber, and many more in a 45-min interview. Learn the RESHADED framework for architecting web-scale applications by determining requirements, constraints, and assumptions before diving into a step-by-step design process.

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