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 […]

Last Updated on December 13, 2019 Raw machine learning data contains a mixture of attributes, some of which are relevant to making predictions. How do you know which features to use and which to remove? The process of selecting features in your data to model your problem is called feature selection. In this post you will discover how to perform feature selection with your machine learning data in Weka. After reading this post you will know: About the importance of feature […]

Last Updated on August 22, 2019 A big benefit of using the Weka platform is the large number of supported machine learning algorithms. The more algorithms that you can try on your problem the more you will learn about your problem and likely closer you will get to discovering the one or few algorithms that perform best. In this post you will discover the machine learning algorithms supported by Weka. After reading this post you will know: The different types of […]

Last Updated on August 22, 2019 The problem of predictive modeling is to create models that have good performance making predictions on new unseen data. Therefore it is critically important to use robust techniques to train and evaluate your models on your available training data. The more reliable the estimate of the performance on your model, the further you can push the performance and be confident it will translate to the operational use of your model. In this post you […]

Last Updated on December 13, 2019 It is really important to have a performance baseline on your machine learning problem. It will give you a point of reference to which you can compare all other models that you construct. In this post you will discover how to develop a baseline of performance for a machine learning problem using Weka. After reading this post you will know: The importance in establishing a baseline of performance for your machine learning problem. How […]