Even the worthy Homer sometimes nods. The idiom means even the most gifted person occasionally makes mistakes. We would adapt this sentence to machine learning lifecycle. Even the best ML-models should make mistakes (or else overfitting problem). The important thing is know how to measeure errors. There are lots of metrics for measuring forecasts. In this post, we will mention evalution metrics meaningful for ML studies.
Homer Simpson uses catchphrase D’oh! when he has done something wrong
Sign of actual and predicted value diffence should not be considered when calculation total error of a system. Otherwise, total error of a series including equally high underestimations and overestimations might measure very low error. In fact, forecasts should include low underestimations and overestimations, and total error should be measured low. Discarding sign values provides to get rid of this negative effect. Squaring differences enables discarding signs. This metric is called as Mean Squared Error or mostly MSE.
Building neural networks models and implementing learning consist of lots of math this might be boring. Herein, some tools help researchers to build network easily. Thus, a researcher who knows the basic concept of neural networks can build a model without applying any math formula.
So, Weka is one of the most common machine learning tool for machine learning studies. It is a java-based API developed by Waikato University, New Zealand. Weka is an acronym for Waikato Environment for Knowledge Analysis. Actually, name of the tool is a funny word play because weka is a bird species endemic to New Zealand. Thus, researchers can introduce an endemic bird to world wide.