How “good” is your model,
and how can you make it better?
PyData London, 2015
What distinguishes “true artists” from “one-hit wonders” in machine learning is an understanding of how a model performs with respect to different data. This hands-on tutorial will show you how to use scikit-learn’s model evaluation functions to evaluate different models in terms of accuracy and generalisability, and search for optimal parameter configurations.
The objective of this tutorial is to give participants the skills required to validate, evaluate and fine-tune models using scikit-learn’s evaluation metrics and parameter search capabilities. It will combine both the theoretical rationale behind these methods and their code implementation. You can find more information and a rough schedule at http://london.pydata.org/schedule/presentation/7/
Required libraries: numpy, scikit-learn, matplotlib, pandas, scipy, multilayer_perceptron (provided from https://github.com/IssamLaradji/NeuralNetworks)