Articles About Machine Learning

Review of Applied Predictive Modeling

Last Updated on August 15, 2020 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 predictive modeler. About the Book Applied Predictive Modeling is written by Max Kuhn and Kjell Johnson. Max Kuhn is […]

September 29, 2020 Machine Learning

Benefits of Implementing Machine Learning Algorithms From Scratch

Last Updated on August 15, 2020 Machine Learning can be difficult to understand when getting started. There are a lot of algorithms and processes that are prescribed and used, many with difficult to penetrate explanations for how and why the work. It can feel overwhelming. An approach that you can use to get handle on machine learning algorithms and practices is to implement them from scratch. This will give you a deep understanding of how the algorithm works and all […]

September 29, 2020 Machine Learning

Caret R Package for Applied Predictive Modeling

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 […]

September 29, 2020 Machine Learning

Data Visualization with the Caret R package

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 […]

September 29, 2020 Machine Learning

Tuning Machine Learning Models Using the Caret R Package

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 […]

September 29, 2020 Machine Learning

Feature Selection with the Caret R Package

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 […]

September 29, 2020 Machine Learning

Compare Models And Select The Best Using The Caret R Package

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 […]

September 29, 2020 Machine Learning

Discover Feature Engineering, How to Engineer Features and How to Get Good at It

Last Updated on August 15, 2020 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 to engineer features, who is doing it well and where you can go to learn more and get good […]

September 29, 2020 Machine Learning

A Data-Driven Approach to Choosing Machine Learning Algorithms

Last Updated on April 4, 2018 If You Knew Which Algorithm or Algorithm Configuration To Use,You Would Not Need To Use Machine Learning There is no best machine learning algorithm or algorithm parameters. I want to cure you of this type of silver bullet mindset. I see these questions a lot, even daily: Which is the best machine learning algorithm? What is the mapping between machine learning algorithms and problems? What are the best parameters for a machine learning algorithm? There […]

September 29, 2020 Machine Learning

How to Build an Intuition for Machine Learning Algorithms

Last Updated on December 13, 2019 Machine learning algorithms are complex. To get good at applying a given algorithm you need to study it from multiple perspectives: algorithmic, mathematical and empirical. It’s this last point I want to stress. You need to build up an intuition or how an algorithm behaves on real data. You need to work on lots of problems. In this post I want to encourage you to use small in-memory datasets when starting out and when […]

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