DataRobot Blog

Following best practices when building machine learning models is a time-consuming yet important process. There are so many things to do ranging from: preparing the data, selecting and training algorithms, understanding how the algorithm is making decisions, all the way down to deploying models to production. I like to think of the machine learning design and maintenance process as being…

***Spoilers Ahead***   This blog is meant to be a fun and unique take on predicting which characters are likely to survive (or die) in the final season of "Game of Thrones".   After finishing season seven of Game of Thrones, the record-breaking HBO show based on George R. R. Martin’s fantasy series A Song of Ice and Fire, we…

This article was originally published at Algorithimia’s website. The company was acquired by DataRobot in 2021. This article may not be entirely up-to-date or refer to products and offerings no longer in existence. Find out more about DataRobot MLOps here. Introduction to loss functions The loss function is the bread and butter of modern machine learning; it takes your algorithm from…

This article was originally published at Algorithimia’s website. The company was acquired by DataRobot in 2021. This article may not be entirely up-to-date or refer to products and offerings no longer in existence. Find out more about DataRobot MLOps here. What is sentiment analysis? Sentiment analysis is the process of using natural language processing, text analysis, and statistics to analyze…

We've gotten some questions recently about how much data is needed to train a good model. While there is no "one-size-fits-all" approach, there are some general best practices to follow and questions to ask about your data beforehand. Ryan elaborates. How much data do you need to train a model? Arguably, only a single data point. Is this model likely…

This article was originally published at Algorithimia’s website. The company was acquired by DataRobot in 2021. This article may not be entirely up-to-date or refer to products and offerings no longer in existence. Find out more about DataRobot MLOps here. This post discusses everything you need to know about NLP—whether you’re a developer, a business, or a complete beginner—and how…

Gradient Boosted Regression Trees (GBRT) or shorter Gradient Boosting is a flexible non-parametric statistical learning technique for classification and regression. This notebook shows how to use GBRT in scikit-learn, an easy-to-use, general-purpose toolbox for machine learning in Python. We will start by giving a brief introduction to scikit-learn and its GBRT interface. The bulk of the tutorial will show how…

This post looks into the problem of classification, a situation in which a response is a categorical variable. We will build upon the techniques that we previously discussed in the context of regression and show how they can be transferred to classification problems. This post introduces a number of classification techniques, and it will try to convey their corresponding strengths…

Earlier we covered Ordinary Least Squares regression with a single variable. In this posting we will build upon that by extending Linear Regression to multiple input variables giving rise to Multiple Regression, the workhorse of statistical learning. We first describe Multiple Regression in an intuitive way by moving from a straight line in a single predictor case to a 2d…