Introduction
A learning model is a parameterized function whose parameters are learned from data. When data is processed through a learning model it's outputs are often approximate relationships or make predictions.
Problem Classes
There are numerous algorithms for predicting continuous variables or categorical variables from a set of continuous predictors and/or categorical factor effects.
Regression-type problems.
Regression-type problems are generally those where we attempt to predict the values of a continuous variable from one or more continuous and/or categorical predictor variables.
Classification-type problems.
Classification-type problems are generally those where we attempt to predict values of a categorical dependent variable (class, group membership, etc.) from one or more continuous and/or categorical predictor variables. There are a number of methods for analyzing classification-type problems and to compute predicted classifications, either from simple continuous predictors (e.g., binomial or multinomial logit regression in GLZ), from categorical predictors (e.g., Log-Linear analysis of multi-way frequency tables), or both (e.g., via ANCOVA-like designs in GLZ or GDA).
Tree-Based Solutions
Tree methods are nonparametric and nonlinear + Simplicity of results. -Specify Criteria for Predictive Accuracy, Selecting Splits, When to Stop Splitting.
Model Types By Statistical Role
Generative
- Gaussian Mixture Models
- Hidden Markov Models
- Naive Bayes
- GANS (when parameterized accordingly)
Discriminative
- NN
- SVM
- Decision Trees
- Logistic Regression
Descriptive
- Clustering (k-means, DBSCAN)
- Association rule mining
- PCA or factor analysis
Learning Model Paradigms
Supervised:
labeled inputs; regression and classification.
Unsupervised
latent structure; clustering, density estimation, manifold learning.
Semi-supervised
joint usage of labeled + unlabeled samples.
Self-supervised
surrogate labels from transformations; pretraining regimes.
Reinforcement learning
sequential decision processes maximizing cumulative reward.