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 […]
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 […]
Optimization is a big part of machine learning. It is the core of most popular methods, from least squares regression to artificial neural networks. In this post you will discover recipes for 5 optimization algorithms in R. These methods might be useful in the core of your own implementation of a machine learning algorithm. You […]
R is perhaps one of the most powerful and most popular platforms for statistical programming and applied machine learning. When you get serious about machine learning, you will find your way into R. In this post, you will discover what R is, where it came from and some of its most important features. Let’s get […]
How do you get good at Kaggle competitions? It is a common question I get asked. The best advice for getting started and getting good is to consistently participate in competitions. You cannot help but get better at machine learning. A recent post by Triskelion titled “Reflecting back on one year of Kaggle contests” bares […]
The predictions you make with a predictive model do not matter, it is the use of those predictions that matters. Jeremy Howard was the President and Chief Scientist of Kaggle, the competitive machine learning platform. In 2012 he presented at the O’reilly Strata conference on what he called the Drivetrain Approach for building “data products” […]
What if you could use a predictive model to find new combinations of attributes that do not exist in the data but could be valuable. In Chapter 10 of Applied Predictive Modeling, Kuhn and Johnson provide a case study that does just this. It’s a fascinating and creative example of how to use a predictive […]
Data preparation can make or break the predictive ability of your model. In Chapter 3 of their book Applied Predictive Modeling, Kuhn and Johnson introduce the process of data preparation. They refer to it as the addition, deletion or transformation of training set data. In this post you will discover the data pre-process steps that […]
Machine learning models are parameterized so that their behavior can be tuned for a given problem. Models can have many parameters and finding the best combination of parameters can be treated as a search problem. In this post, you will discover how to tune the parameters of machine learning algorithms in Python using the scikit-learn […]
Not all data attributes are created equal. More is not always better when it comes to attributes or columns in your dataset. In this post you will discover how to select attributes in your data before creating a machine learning model using the scikit-learn library. Let’s get started. Update: For a more recent tutorial on feature selection in […]
