Assessing machine learning algorithm performance

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.

• 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.