Project for learning data generation using Python
This project aims to acheive generating testing data using scikit learn in python. The data from test datasets have well-defined properties, such as linearly or non-linearity, that allow you to explore specific algorithm behavior. The scikit-learn Python library provides a suite of functions for generating samples from configurable test problems for regression and classification.
A problem when developing and implementing machine learning algorithms is how do you know whether you have implemented them correctly. They seem to work even with bugs.
Test datasets are small contrived problems that allow you to test and debug your algorithms and test harness. They are also useful for better understanding the behavior of algorithms in response to changes in hyperparameters.