ML Series8: Evaluation

How to know if the model is working?

We’ve reviewed some of the most representative modern machine learning algorithms in the previous series. These can get us a good start to build supervised predictive models. Only learning about the models is not enough, since you can’t manage something you can’t measure. This post will focus on the performance measure, how good is the model and how to understand it.

Regression

Mean Squared Error (MSE)

Root Mean Squared Error (RMSE)

Mean Absolute Error (MAE)

Mean Absolute Percentage Error (MAPE)

R² or Coefficient of Determination

Classification

A good picture to remember

Above are very useful summary statistics. Also useful to check confusion matrix.

Error Decomposition for Measure Squared Error

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