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.
