Last Updated on December 11, 2019 It is a good idea to have small well understood datasets when getting started in machine learning and learning a new tool. The Weka machine learning workbench provides a directory of small well understood datasets in the installed directory. In this post you will discover some of these small well understood datasets distributed with Weka, their details and where to learn more about them. We will focus on a handful of datasets of differing […]

Last Updated on August 22, 2019 It is important to take your time to learn about your data when starting on a new machine learning problem. There are key things that you can look at to very quickly learn more about your dataset, such as descriptive statistics and data visualizations. In this post you will discover how you can learn more about your data in the Weka machine learning workbench my reviewing descriptive statistics and visualizations of your data. After […]

Last Updated on December 11, 2019 Machine learning algorithms make assumptions about the dataset you are modeling. Often, raw data is comprised of attributes with varying scales. For example, one attribute may be in kilograms and another may be a count. Although not required, you can often get a boost in performance by carefully choosing methods to rescale your data. In this post you will discover how you can rescale your data so that all of the data has the […]

Last Updated on December 13, 2019 Often your raw data for machine learning is not in an ideal form for modeling. You need to prepare or reshape it to meet the expectations of different machine learning algorithms. In this post you will discover two techniques that you can use to transform your machine learning data ready for modeling. After reading this post you will know: How to convert a real valued attribute into a discrete distribution called discretization. How to […]

Last Updated on December 13, 2019 Data is rarely clean and often you can have corrupt or missing values. It is important to identify, mark and handle missing data when developing machine learning models in order to get the very best performance. In this post you will discover how to handle missing values in your machine learning data using Weka. After reading this post you will know: How to mark missing values in your dataset. How to remove data with […]