Last Updated on August 22, 2019 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 R and it can feel overwhelming. For the purposes of applied machine learning, the caret package provides a few […]

Last Updated on August 22, 2019 Machine learning algorithms are parameterized so that they can be best adapted for a given problem. A difficulty is that configuring an algorithm for a given problem can be a project in and of itself. Like selecting ‘the best’ algorithm for a problem you cannot know before hand which algorithm parameters will be best for a problem. The best thing to do is to investigate empirically with controlled experiments. The caret R package was […]

Last Updated on August 22, 2019 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 post you will discover the feature selection tools in the Caret R package with standalone recipes in R. After […]

Last Updated on December 13, 2019 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 the results of multiple models using the caret R package. Kick-start your project with my new book Machine Learning […]