Comparing machine learning models 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...
Quora explanation of overfitting: http://www.quora.com/What-is-an-intui...
Estimating prediction error:    • Видео  
Understanding the Bias-Variance Tradeoff: http://scott.fortmann-roe.com/docs/Bi...
Guiding questions for that article: https://github.com/justmarkham/DAT8/b...
Visualizing bias and variance: http://work.caltech.edu/library/081.html

WANT TO GET BETTER AT MACHINE LEARNING? HERE ARE YOUR NEXT STEPS:

1) WATCH my scikit-learn video series:
   • Machine learning in Python with sciki...  

2) SUBSCRIBE for more videos:
https://www.youtube.com/dataschool?su...

3) JOIN "Data School Insiders" to access bonus content:
  / dataschool  

4) ENROLL in my Machine Learning course:
https://www.dataschool.io/learn/

5) LET'S CONNECT!
Newsletter: https://www.dataschool.io/subscribe/
Twitter:   / justmarkham  
Facebook:   / datascienceschool  
LinkedIn:   / justmarkham