Data reduction techniques aim to simplify the training data by removing noisy and redundant data,

so that, AI and data science algorithms can learn faster with little or no performance degradation,

as if the entire training set T is used.

The ENN algorithm starts with S$=$T$,$ and then each instance s in S is removed from S if it does not

agree with the majority of kNN $($e$.$g$.$ k$=3$ or k$=5).$ The ENN discards noisy instances as well as

border instances to yield smooth boundaries between classes by saving interior instances.

In Python, you can use the $$ library to generate synthetic datasets for AI purposes.

Specifically, $..\_$ is a handy method for generating a

random n$-$class classification problem. This function allows you to specify the number of samples,

number of features $($vector length$),$ and the number of classes among other parameters.

Here's an example code snippet that uses $\_$ to generate a dataset with $5000$

samples, each with $20$ features, and $7$ different classes: