| Regression learning |
Models a relationship between input and output data (or variables). The relation is iteratively refined by measuring errors in the model’s predictions. |
Variations such as linear and logistic regression |
| Instance-based learning |
Models a decision based on instances of input data that are considered relevant or necessary. Creates a database of reference examples used to compare with new data to find optimal matches using similarity metrics to make a decision. |
K-nearest neighbor and support vector machines |
| Regularization learning |
The extension or modification of another model (eg, regression learning) in a way that reduces the complexity of the model by converting it into a simpler form. |
Ridge regression and elastic net regression |
| Decision tree learning |
Models a decision based on the values of the input data attributes. It follows a tree structure in making a decision for given input data. |
Classification and regression trees and conditional decision tree |
| Bayesian learning |
The models use Bayes’ theorem to solve problems of classification and regression. |
Naïve Bayes and Gaussian naïve Bayes |
| Clustering learning |
The model organizes the input data into groups (or clusters) where group membership or commonality criteria are taken or derived from the data (eg, centroid based or hierarchical). |
K-means, K-medians, and hierarchical clustering |
| Association rule learning |
The model discovers associations in input data to make a decision. It extracts rules that describe relationships between observed variables in input data. |
A priori algorithm and Eclat algorithm |
| Artificial neural network |
The model is driven by the structure and function of the human neural networks. Represents a class of pattern matching models and their commonly used variations for regression and classification problems. |
Perceptron, multilayer perceptrons, and back propagation |
| Deep learning |
Special category of large and complex neural networks for handling vast amounts of labeled input data, including text, images, audio, and video. |
Convolutional neural network, recurrent neural networks, and long short-term memory networks |
| Dimensionality reduction learning |
The model analyzes the input structure in the data to represent and describe the data with less information. The simplified data can be visualized and used by other learning methods. |
Principal component analysis, principal component regression, and linear discriminant analysis |
| Ensemble learning |
Multiple models that are independently trained, where individual predictions are combined to make the final prediction. The models are combined owing to their weaknesses in making the desired prediction. |
Boosting, random forest, AdaBoost, and weighted average (blending) |
| Natural language processing |
Specific for conversational artificial intelligence and includes natural language understanding, dialog management, and natural language generation. |
Rule-based algorithms, statistics, neural networks, and deep learning |
