machine learning on Ethan Rosenthal
https://www.ethanrosenthal.com/tags/machine-learning/
Recent content in machine learning on Ethan RosenthalHugo -- gohugo.ioen-USMon, 18 Feb 2019 00:00:00 +0000Time Series for scikit-learn People (Part III): Horizon Optimization
https://www.ethanrosenthal.com/2019/02/18/time-series-for-scikit-learn-people-part3/
Mon, 18 Feb 2019 00:00:00 +0000https://www.ethanrosenthal.com/2019/02/18/time-series-for-scikit-learn-people-part3/In my previous posts in the “time series for scikit-learn people” series, I discussed how one can train a machine learning model to predict the next element in a time series. Often, one may want to predict the value of the time series further in the future. In those posts, I gave two methods to accomplish this. One method is to train the machine learning model to specifically predict that point in the future.spacecutter: Ordinal Regression Models in PyTorch
https://www.ethanrosenthal.com/2018/12/06/spacecutter-ordinal-regression/
Thu, 06 Dec 2018 00:00:00 +0000https://www.ethanrosenthal.com/2018/12/06/spacecutter-ordinal-regression/How would you build a machine learning algorithm to solve the following types of problems?
Predict which medal athletes will win in the olympics. Predict how a shoe will fit a foot (too small, perfect, too big). Predict how many stars a critic will rate a movie. If you reach into your typical toolkit, you’ll probably either reach for regression or multiclass classification. For regression, maybe you treat the number of stars (1-5) in the movie critic question as your target, and you train a model using mean squared error as your loss function.Time Series for scikit-learn People (Part II): Autoregressive Forecasting Pipelines
https://www.ethanrosenthal.com/2018/03/22/time-series-for-scikit-learn-people-part2/
Thu, 22 Mar 2018 00:00:00 +0000https://www.ethanrosenthal.com/2018/03/22/time-series-for-scikit-learn-people-part2/In this post, I will walk through how to use my new library skits for building scikit-learn pipelines to fit, predict, and forecast time series data. We will pick up from the last post where we talked about how to turn a one-dimensional time series array into a design matrix that works with the standard scikit-learn API. At the end of that post, I mentioned that we had started building an ARIMA model.Time Series for scikit-learn People (Part I): Where's the X Matrix?
https://www.ethanrosenthal.com/2018/01/28/time-series-for-scikit-learn-people-part1/
Sun, 28 Jan 2018 00:00:00 +0000https://www.ethanrosenthal.com/2018/01/28/time-series-for-scikit-learn-people-part1/When I first started to learn about machine learning, specifically supervised learning, I eventually felt comfortable with taking some input $\mathbf{X}$, and determining a function $f(\mathbf{X})$ that best maps $\mathbf{X}$ to some known output value $y$. Separately, I dove a little into time series analysis and thought of this as a completely different paradigm. In time series, we don’t think of things in terms of features or inputs; rather, we have the time series $y$, and $y$ alone, and we look at previous values of $y$ to predict future values of $y$.Towards optimal personalization: synthesisizing machine learning and operations research
https://www.ethanrosenthal.com/2016/08/30/towards-optimal-personalization/
Tue, 30 Aug 2016 00:00:00 +0000https://www.ethanrosenthal.com/2016/08/30/towards-optimal-personalization/Last post I talked about how data scientists probably ought to spend some time talking about optimization (but not too much time - I need topics for my blog posts!). While I provided a basic optimization example in that post, that may have not been so interesting, and there definitely wasn’t any machine learning involved.
Right now, I think that the most exciting industrial applications of optimization are those that synthesize machine learning and optimization in order to obtain optimal personalization at scale.