**January 9, 2021**

Bootstrapping is a subset of resampling techniques, here different subsets of data are sampled with replacement i.e. an observation can be present in more than one subset. It particularly useful for small datasets, where resampling virtually increases the amount of available data.

Sampling in Bootstrapping is based on two principles:

**Random Selection**: Each subset is chosen on a random basis. The level of randomness is such that more often than not some rows are not able to make it to any of the subsets.**Sampling with Replacement:**An observation can be sampled more than one i.e. it can be present in multiple subsets of the data simultaneously.

by : Monis Khan

**Quick Summary**:

Bootstrapping is a subset of resampling techniques, here different subsets of data are sampled with replacement i.e. an observation can be present in more than one subset. It particularly useful for small datasets, where resampling virtually increases the amount of available data. Sampling in Bootstrapping is based on two principles: Random Selection: Each subset is […]