Last Updated on August 15, 2020 When you are building a predictive model, you need a way to evaluate the capability of the model on unseen data. This is typically done by estimating accuracy using data that was not used to train the model such as a test set, or using cross validation. The caret package in R provides a number of methods to estimate the accuracy of a machines learning algorithm. In this post you discover 5 approaches for […]

Last Updated on August 22, 2019 R is the most popular platform for applied machine learning. When you want to get serious with applied machine learning you will find your way into R. It is very powerful because so many machine learning algorithms are provided. A problem is that the algorithms are all provided by third parties, which makes their usage very inconsistent. This slows you down, a lot, because you have to learn how to model data and how […]

Last Updated on August 22, 2019 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 caret package in R, it’s key features and where to go to learn more about it. Kick-start your project […]

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 […]