Supervised machine learning

Supervised machine learning is a type of machine learning in which the input is pre-labeled with the output to be predicted by the model. Supervised learning consists of classification learning, which predict qualitative/categorical values, and regression learning, which predict continuous values.

Supervised learning model evaluation

Typically, when a supervised machine learning model is trained, some portion of the training data is withheld for use in model evaluation.

The model is then used to predict the withheld data. The predictions are then compared to the actual values to derive an accuracy rate, which represents the overall accuracy of the model, and an error rate which represents the number of "bad" predictions made by the model.

Accuracy and error rates are useful; however, they treat all misclassifications as being equally bad. A confusion matrix plots the misclassifications to provide more detail on model accuracy.

For example, we may have a classification model that predicts whether a user will "like" or "dislike" a post on social media in which the model accurately predicts the user's input 60% of the time. The model therefore has a 60% accuracy rate and a 40% error rate. The confusion matrix for this model might look something like the following table, illustrating that the model performs better for predicting "dislike" classes than "like" classes.

Deeper Knowledge on Supervised Machine Learning

Semisupervised Learning

Machine learning that uses partially labeled input as training data

Regression Learning

A type of machine learning that classifies entities based on their characteristics

Classification Learning

A type of machine learning that classifies entities based on their characteristics

Broader Topics Related to Supervised Machine Learning

Types of Machine Learning

An overview of the types of machine learning