Machine Learning Taxonomy

Models · Algorithms · Accuracy Metrics · Complete Edition

🤖 Machine Learning
Supervised
Regression / Continuous
Linear Regression
Polynomial Regression
Ridge Regression (L2)
Lasso Regression (L1)
Elastic Net
Decision Tree Regression
Random Forest Regression
Gradient Boosting Regressor
XGBoost Regressor
CatBoost Regressor
Support Vector Regression (SVR)
Supervised
Classification
Logistic Regression
Linear Discriminant Analysis (LDA)
k-Nearest Neighbor (kNN)
Support Vector Machine (SVM)
Naive Bayes
Decision Tree Classifier
Random Forest Classifier
AdaBoost
XGBoost Classifier
LightGBM Classifier
CatBoost Classifier
Artificial Neural Network (ANN)
Unsupervised
Clustering · Dim. Reduction · Anomaly
── Clustering ──
K-Means Clustering
DBSCAN
Hierarchical Clustering
Gaussian Mixture Model (GMM)
── Dimensionality Reduction ──
Principal Component Analysis (PCA)
t-SNE
UMAP
Autoencoders
── Anomaly Detection ──
Isolation Forest
Local Outlier Factor (LOF)
One-Class SVM
Association
Rule Learning · Pattern Mining
Apriori Algorithm
FP-Growth
ECLAT
Semi-Supervised
Few Labels + Unlabeled Data
Self-Training
Label Propagation
Co-Training
Pseudo-Labeling
Semi-Supervised GAN
Deep Learning
Neural Architectures
Convolutional Neural Network (CNN)
RNN / LSTM / GRU
Transformer
Generative Adversarial Network (GAN)
Variational Autoencoder (VAE)
Graph Neural Network (GNN)
Reinforcement
Agent + Environment
Q-Learning
Deep Q-Network (DQN)
Policy Gradient (REINFORCE)
Proximal Policy Optimization (PPO)
A3C / A2C
Soft Actor-Critic (SAC)
DDPG / TD3
Monte Carlo Methods
Supervised — Regression
Supervised — Classification
Unsupervised
Association Rule Learning
Semi-Supervised
Deep Learning
Reinforcement Learning
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Use Case
Example
Pros
Cons
Eval. Metric