I wrote a few quick bullet points down from the article “How To Implement Machine Learning Algorithm Performance Metrics From Scratch With Python“.
Metrics
 Classification accuracy
 Test how well predictions of a model do overall
 accuracy = correct predictions / total predictions
 Confusion matrix
 Use to identify how well your predictions did with different classes
 Very useful if you have an imbalanced dataset
 I wrote an extremely hacked together confusion matrix for my tag identification software. I had 4 classes (U, C, R, Q) and the confusion matrix shows you what your model predicted against what the real category was.
U 
C 
R 
Q 

U 
175 
17 
67 
1 
C 
11 
335 
14 
0 
R 
26 
8 
298 
0 
Q 
6 
0 
3 
93 
 Mean absolute error for regression
 Positive values – the average of how much your predicted value differ from the real value
 Root mean squared error for regression
 Square root of the mean of squared differences between the actual and predicted value
 Squaring the values gives you positive numbers and finding the root lets you compare the values to the original units.