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:

  1. Accuracy
  2. Precision
  3. Recall
  4. F1 Score
  5. Logistic loss (also known as Cross-entropy loss)
  6. Jaccard similarity coefficient score

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