How To Estimate The Performance of Machine Learning Algorithms in Weka

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 will discover the various different ways that you can estimate the performance of your machine learning models in Weka.

After reading this post you will know:

• How to evaluate your model using the training dataset.
• How to evaluate your model using a random train and test split.
• How to evaluate your model using k-fold cross validation.

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