We all know the satisfaction of running an analysis and seeing the results come back the way we want them to: 80% accuracy; 85%; 90%? The temptation is strong just to turn to the Results section of the report we’re writing, and put the numbers in. But wait: as always, it’s not that straightforward. Succumbing […]
In my courses and guides, I teach the preparation of a baseline result before diving into spot checking algorithms. A student of mine recently asked: If a baseline is not calculated for a problem, will it make the results of other algorithms questionable? He went on to ask: If other algorithms do not give better accuracy […]
Which features should you use to create a predictive model? This is a difficult question that may require deep knowledge of the problem domain. It is possible to automatically select those features in your data that are most useful or most relevant for the problem you are working on. This is a process called feature […]
What does it take to do well in competitive machine learning? To really dig into this question, you need to dig into the people that do well. In 2010 I participated in a Kaggle competition to predict the outcome of chess games in the future. It was a fascinating problem because it required you to […]
Feature engineering is an informal topic, but one that is absolutely known and agreed to be key to success in applied machine learning. In creating this guide I went wide and deep and synthesized all of the material I could. You will discover what feature engineering is, what problem it solves, why it matters, how […]
The Caret R package allows you to easily construct many different model types and tune their parameters. After creating and tuning many model types, you may want know and select the best model so that you can use it to make predictions, perhaps in an operational environment. In this post you discover how to compare […]
Selecting the right features in your data can mean the difference between mediocre performance with long training times and great performance with short training times. The caret R package provides tools to automatically report on the relevance and importance of attributes in your data and even select the most important features for you. In this […]
The caret package in R is designed to streamline the process of applied machine learning. A key part of solving data problems in understanding the data that you have available. You can do this very quickly by summarizing the attributes with data visualizations. There are a lot of packages and functions for summarizing data in […]
The R platform for statistical computing is perhaps the most popular and powerful platform for applied machine learning. The caret package in R has been called “R’s competitive advantage“. It makes the process of training, tuning and evaluating machine learning models in R consistent, easy and even fun. In this post you will discover the […]
The book Applied Predictive Modeling teaches practical machine learning theory with code examples in R. It is an excellent book and highly recommended to machine learning practitioners and users of R for machine learning. In this post you will discover the benefits of this book and how it can help you become a better machine […]
