Anthony Gitter

@cmilica found http://www.r2d3.us/ with great visualizations for decision trees and bias-variance tradeoffs.

We may also want to consider a more complex dataset that has more features to introduce overfitting.

> Logisitic regression did not give a solution with L1 regularization that ignored one of the features @cmilica and I discussed this example today. The logistic regularization regularization example uses...

I was curious whether the T cell dataset with CellProfiler features would be a good example dataset. Unfortunately, it is not. Here are the validation set results when varying the...

We may be better off using a real dataset to illustrate overfitting. The [Penn ML benchmarks](https://epistasislab.github.io/penn-ml-benchmarks/) could be one possible source.

There is a Kaggle rental properties datasets that is similar to the hypothetical housing example we use to introduce machine learning: https://www.kaggle.com/arashnic/property-data

It is possible to train a logistic regression classifier that ignores one or both of the features in the example dataset with a suitable value of C. However, we need...

After changing the values of C in c82fe8fc75eb3804c395460edc9c5ed944fcd5e4, the logistic regression example now shows 0, 1, or 2 features with non-zero feature weights in the L1 regularization example. This may...

I think the suggestion was to make the subsection headers in the lessons the verb originating phrases as well? So, for example, in the into we could make "Group activity"...

Thanks for fixing the MathJax configuration. I think adding the `{% include mathjax.html %}` statement into `_layouts/episode.html` makes sense for now. If we need MathJax in the glossary or other...