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