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