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pydata-tutorial

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# How “good” is your model, <br/> and how can you make it better?
*PyData London, 2015* <br/>

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.<br/>

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/ <br/>

**Required libraries**: numpy, scikit-learn, matplotlib, pandas, scipy, multilayer_perceptron (provided from https://github.com/IssamLaradji/NeuralNetworks)

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