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.
The following two tabs change content below.
Currently fascinated by energy markets and electrical engineering.
In another life I was a beekeeper that did a PhD in computational biology writing image analysis software and using machine learning to quantify honeybee behaviour in the hive.
Latest posts by Jack Simpson (see all)
- Exploring the impact of constraints on a solar farm in the National Electricity Market - September 3, 2021
- Optimisation and Energy Modelling Talks From JuliaCon 2021 - July 29, 2021
- How does AEMO predict demand in the National Electricity Market? - July 26, 2021
