What is classification? Which models would you use to solve a classification problem?
Classification problems are problems in which our prediction space is discrete, i.e. there is a finite number of values the output variable can be. Some models which can be used to solve classification problems are: logistic regression, decision tree, random forests, multi-layer perceptron, one-vs-all, amongst others.
How do we evaluate classification models?
Depending on the classification problem, we can use the following evaluation metrics:
- F1 Score
- Logistic loss (also known as Cross-entropy loss)
- Jaccard similarity coefficient score