Colorado Reed from Metacademy wrote a great post recently titled “Level-Up Your Machine Learning” to answer the question he often receives of: What should I do if I want to get ‘better’ at machine learning, but I don’t know what I want to learn? In this post you will discover a summary of Colorado recommendations […]
In this post you will discover 8 recipes for non-linear classification in R. Each recipe is ready for you to copy and paste and modify for your own problem. All recipes in this post use the iris flowers dataset provided with R in the datasets package. The dataset describes the measurements if iris flowers and requires classification of […]
In this post you will discover recipes for 3 linear classification algorithms in R. All recipes in this post use the iris flowers dataset provided with R in the datasets package. The dataset describes the measurements if iris flowers and requires classification of each observation to one of three flower species. Let’s get started. Logistic […]
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 […]
In their book Applied Predictive Modeling, Kuhn and Johnson comment early on the trade-off of model prediction accuracy versus model interpretation. For a given problem, it is critical to have a clear idea of the which is a priority, accuracy or explainability so that this trade-off can be made explicitly rather than implicitly. In this […]
A valuable exercise when learning and practicing machine learning is to study how others apply methods and solve problems. It’s valuable because you can learn about new processes, software, graphs, and algorithms. But it is new ways of thinking about the process of solving problems with machine learning that is the most valuable part of […]
Are you a Java programmer and looking to get started or practice machine learning? Writing programs that make use of machine learning is the best way to learn machine learning. You can write the algorithms yourself from scratch, but you can make a lot more progress if you leverage an existing open source library. In […]
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 […]
Your data must be prepared before you can build models. The data preparation process can involve three steps: data selection, data preprocessing and data transformation. In this post you will discover two simple data transformation methods you can apply to your data in Python using scikit-learn. Let’s get started. Update: See this post for a […]
