The sheer number of model evaluation techniques available to asses how good your model is can be completely overwhelming. As well as the oft-used confidence intervals, confusion matrix and cross validation, there are dozens more that you could use for specific situations, including McNemar's test, Cochran's Q, Multiple Hypothesis testing and many more. This one picture whittles down that list to a dozen or so of the most popular. You'll find links to articles explaining the specific tests and procedures below the image.
General:
Regression:
Correlation and R-Squared for Big Data (RMSE L2 version)
R-Squared & Adjusted R-Squared
Classification Visuals:
Understanding And Interpreting Gain And Lift Charts
Classification Statistics and Tools:
11 Important Model Evaluation Techniques Everyone Should Know
Introduction to Machine Learning Model Evaluation
Model Evaluation: Classification
Model Evaluation: Regression Models
What is Predictive Models Performance Evaluation and Why it is Impo...
Gain and Lift Image: Understanding And Interpreting Gain And Lift Charts
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