Supervised LearningIt is a machine learning method in which the network is trained using examples for which the answers are known, and there is a relationship between the output and the input.

Patterns can usually be learned or established based on training data and used to infer new instances, where the training data consists of inputs and expected outputs. The output of the function can be a continuous value or a predicted classification.

Classification of supervised learning

Supervised learning is often used for classification and regression problems. The difference between regression and classification algorithms lies in the type of output variables, where regression specifies quantitative output or continuous variable prediction; classification specifies type output or discrete variable prediction.

Classification of Machine Learning Methods

There are three main approaches in the field of machine learning:

• Supervised learning: training with examples of known answers;
• Unsupervised learning: directly modeling the input dataset, such as clustering;
• Semi-supervised learning: Comprehensively utilize labeled data and unlabeled data to generate appropriate functions.

Commonly used supervised learning

• K-nearest neighbor algorithm
• Decision Tree
• Naive Bayes
• Logistic Regression
• Backward propagation neural network

Parent word: machine learning

Synonyms: semi-supervised learning, unsupervised learning

Sub-words: regression algorithm, classification algorithm