What distinguishes a binary from a multi-class classification model?

1 Binary classification

A binary classification model is a model that can only distinguish between two classes, such as yes or no, positive or negative, spam or not spam, etc. For example, if you want to build a model that can detect whether an email is spam or not, you would use a binary classification model. A binary classification model usually outputs a probability score between 0 and 1 for each input, and then applies a threshold to decide which class to assign. For example, if the score is greater than 0.5, the input is classified as positive, otherwise as negative.

2 Multi-class classification

A multi-class classification model is a model that can distinguish between more than two classes, such as red, green, or blue, dog, cat, or bird, etc. For example, if you want to build a model that can recognize the type of animal in an image, you would use a multi-class classification model. A multi-class classification model usually outputs a vector of probability scores for each input, one for each class, and then selects the class with the highest score as the prediction. For example, if the scores are [0.2, 0.6, 0.2] for red, green, and blue, the input is classified as green.

3 One-vs-all and one-vs-one

One challenge of multi-class classification is that some algorithms are designed for binary classification only, such as logistic regression or support vector machines. How can you use these algorithms for multi-class problems? One solution is to use one-vs-all or one-vs-one strategies. One-vs-all means that you train one binary classifier for each class, where the positive class is the target class and the negative class is the rest of the classes. Then, you use all the classifiers to score an input and pick the one with the highest score. One-vs-one means that you train one binary classifier for each pair of classes, and then use a voting scheme to decide the final prediction.

4 Performance metrics

Another difference between binary and multi-class classification models is how you measure their performance. For binary classification, you can use metrics such as accuracy, precision, recall, F1-score, ROC curve, or AUC. These metrics are based on the confusion matrix, which compares the actual and predicted classes. For multi-class classification, you can use similar metrics, but you need to specify how to handle multiple classes. For example, you can use micro-averaging or macro-averaging to combine the scores for each class, or use a weighted average based on the class frequency.

5 Data imbalance

A common challenge in both binary and multi-class classification models is data imbalance, which means that some classes have more samples than others. This can affect the model's ability to learn from the data and generalize to new inputs. For example, if you have a binary classification problem where 90% of the samples are negative and 10% are positive, the model may learn to always predict negative and ignore the positive class. Similarly, if you have a multi-class classification problem where some classes are very rare and others are very common, the model may learn to favor the common classes and neglect the rare ones. To deal with data imbalance, you can use techniques such as oversampling, undersampling, or synthetic data generation to balance the class distribution.

6 Model selection

The final difference between binary and multi-class classification models is how you select the best model for your problem. There is no one-size-fits-all solution, as different models may have different strengths and weaknesses depending on the data and the task. However, some general guidelines are to consider the following factors: the number and nature of the classes, the size and quality of the data, the complexity and interpretability of the model, the computational resources and time available, and the evaluation criteria and goals. You can also use techniques such as cross-validation, grid search, or random search to compare and optimize different models and parameters.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?