Machine learning/Supervised Learning

Supervised learning is a type of machine learning algorithm that uses a known dataset (called the training dataset) to make predictions.

Supervised learning includes two categories of algorithms:


 * Classification: for categorical response values, where the data can be separated into specific “classes”


 * Regression: for continuous-response values

Classification

 * Support vector machines (SVM)
 * Neural networks
 * Linear classifiers: a group of algorithms such as:
 * Logistic regression
 * Perceptron
 * Fisher's linear discrimination
 * Naïve Bayes classifier
 * Decision trees: a group of algorithms such as
 * Random forest
 * Bootstrap aggregation
 * Boosting
 * Discriminant analysis
 * Nearest neighbors (kNN): A Non-parametric and instance-based method used for classification and regression

Regression

 * Linear regression
 * Nonlinear regression
 * Generalized linear models
 * Decision trees: a group of algorithms such as
 * Random forest
 * Bootstrap aggregation
 * Boosting
 * Neural networks