Bootstrapping using Python

What it does

The bootstrap method is a resampling technique used to estimate statistics on a population by sampling a dataset with replacement. It is used in applied machine learning to estimate the skill of machine learning models when making predictions on data not included in the training data.

How I built it

This procedure of using the bootstrap method to estimate the skill of the model can be summarized as follows:

1. Choose a number of bootstrap samples to perform
2. Choose a sample size
3. For each bootstrap sample
4. Draw a sample with replacement with the chosen size
5. Fit a model on the data sample
6. Estimate the skill of the model on the out-of-bag sample.
7. Calculate the mean of the sample of model skill estimates. I have also used Cross-Validation and the Leave-One-Out Cross-Validation. Cross-validation is a technique for evaluating ML models by training several ML models on subsets of the available input data and evaluating them on the complementary subset of the data. In k-fold cross-validation, you split the input data into k subsets of data (also known as folds).

The Leave-One-Out Cross-Validation, or LOOCV, procedure is used to estimate the performance of machine learning algorithms when they are used to make predictions on data not used to train the model.

What I learned

Analyzed the different accuracies that I got after running each method on the dataset.

Built With

• anaconda
• jupyter
• python