Evaluation Result Machine Learning. They influence how you weight the importance of different characteristics in the results and your ultimate choice of which algorithm to choose. Imbalance classes are very common in machine learning scenarios and hence accuracy might not always be the right evaluation method to look at.
Once you have defined your problem and prepared your data you need to apply machine learning algorithms to the data in order to solve your problem. You build a model get feedback from metrics make improvements and continue until you achieve a desirable accuracy. In this post well focus on the more common supervised learning problems.
Model evaluation aims to estimate the generalization accuracy of a model on future unseenout-of-sample data.
It indicates how successful the scoring predictions of a dataset has been by a trained model. This topic explains how to visualize and interpret prediction results in Azure Machine Learning Studio classic. You build a model get feedback from metrics make improvements and continue until you achieve a desirable accuracy. The idea of building machine learning models works on a constructive feedback principle.