How to Choose the Best Machine Learning Model in scikit-learn 🚀

We've learned how to train different machine learning models and make predictions, but how do we actually choose which model is "best"? We'll cover the train/test split process for model evaluation, which allows you to avoid "overfitting" by estimating how well a model is likely to perform on new data. We'll use that same process to locate optimal tuning parameters for a KNN model, and then we'll re-train our model so that it's ready to make real predictions.

Download the notebook: https://github.com/justmarkham/scikit-learn-videos
Quora explanation of overfitting: http://www.quora.com/What-is-an-intuitive-explanation-of-overfitting/answer/Jessica-Su
Estimating prediction error: https://www.youtube.com/watch?v=_2ij6eaaSl0&t=2m34s
Understanding the Bias-Variance Tradeoff: http://scott.fortmann-roe.com/docs/BiasVariance.html
Guiding questions for that article: https://github.com/justmarkham/DAT8/blob/master/homework/09_bias_variance.md
Visualizing bias and variance: http://work.caltech.edu/library/081.html

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