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Deterministic Models
Stochastic
Agent-based Models
Linear Models - Simple/Multiple linear Regression
Generalized Linear Models - Logistic/Poisson/Negative Binomial Regression
Generalized Additive Models (GAMs)
Mixed Effects Models / Hierarchal Models - (Generalized) Linear Mixed Models
Survival Models - Cox Proportional Hazards Model, Kaplan-Meier Estimator, Accelerated Failure Time Model
Time Series Models - Autoregressive Models, Moving Average Model, AutoRegressive Integrated Moving Average Model (ARMA/ARIMA), Seasional ARIMA
Multivariate Models - Multivariate ANOVA, Canonical Correlation Analysis (CCA), Factor Analysis, Principal Component Analysis (PCA)
Bayesian Models -
Nonparametric Models - Kernal Density Estimation, Kruskal-Wallis/ Wilcoxon Tests
Classification Models -
Supervised Machine Learning Models
Regression & Classification
Regression
Linear Regression
Ridge/Lasso/Elastic Net Regression
Decision Trees for Regression
Random Forest Regression
Support Vector Regression
Gradient Boosting Regression
K-Nearest Neighbors Regression
Classification:
Logistic Regression
K-Nearest Neighbors (KNN)
Decision Trees
Random Forest
Gradient Boosting (XGBoost, LightGBM, CatBoost)
Support Vector Machines (SVM)
Naive Bayes (Gaussian/Multinomial/Bernoulli)
Bayesian Networks
Regression
Linear / Ridge / Lasso
Random Forest / Gradient Boosting
Neural Networks (for modeling complex relationships)
Classification
Logistic Regression
Random Forest
Gradient Boosting (e.g., XGBoost)
SVM
Neural Networks (for high-dimensional data like genomics)
Regression
LSTMs / GRUs
ARIMA (classical time series model)
Gradient Boosting / Random Forest
Classification
Recurrent Neural Networks (RNNs, LSTMs, GRUs)
1D CNNs
Random Forest / Gradient Boosting on extracted features
Regression
Convolutional Neural Nets for Regression
Linear/MLP Models on extracted features
Classification
Convolutional Neural Nets
Transfer Learning (ResNet, VGG)
Support Vector Regression on extracted features
KNN on pixel or embedding data
Regression
Linear Regression (Numeric Features)
Neural Networks with embeddings
Classification
Naive Bayes (Multinomial/Gaussian)
Classification
Naive Bayes (Multinomial/Gaussian)
Logistic Regression (with TF-IDF or embeddings)
Support Vector Machines (SVM)
Gradient Boosting (on features like TF-IDF)
Recurrent Neural Networks (RNNs, LSTMs)
Transformers (e.g., BERT fine-tuned for classification)
Unsupervised Machine Learning Models
Clustering, Dimensionality Reduction & Anomaly Detection
Clustering:
K-Means
Hierarchical Clustering
DBSCAN
Gaussian Mixture Models (GMM)
Spectral Clustering
Birch
Dimensionality Reduction:
PCA
t-SNE
UMAP
Autoencoders
Anomaly Detection:
Isolation Forest
One-Class SVM
Local Outlier Factor (LOF)
Elliptic Envelope
Dimensionality Reduction:
PCA (commonly used for exploratory analysis)
t-SNE / UMAP (for visualizing high-dimensional datasets)
Autoencoders (deep representation learning)
Factor Analysis (for identifying latent variables)
Clustering:
Gaussian Mixture Models (GMM)
K-Means / Hierarchical Clustering (e.g., patient stratification)
DBSCAN (for irregularly shaped biological data clusters)
Anomaly Detection:
Isolation Forest (e.g., for outlier patient detection)
Autoencoders (for subtle anomalies)
One-Class SVM
Clustering:
K-Means on extracted features (e.g., MFCC for audio)
Dynamic Time Warping (DTW)-based clustering
Hierarchical Clustering
GMM (for sound types or patterns)
Dimensionality Reduction:
PCA / t-SNE / UMAP (on features like FFT, spectrograms)
Autoencoders / Sequence Autoencoders
Anomaly Detection:
Isolation Forest / One-Class SVM / LOF
Autoencoders (based on reconstruction error)
Recurrent Autoencoders (for time-series patterns)
Clustering:
K-Means / GMM on pixel or embedding space
DBSCAN (for segmenting or grouping similar images)
Self-supervised learning methods (e.g., SimCLR + clustering)
Dimensionality Reduction:
Autoencoders (Vanilla, Variational)
PCA (on flattened images or CNN features)
t-SNE / UMAP (for visualizing image embeddings)
Generative Models:
GANs (Generative Adversarial Networks)
VAEs (Variational Autoencoders)
RBMs
Topic Modeling:
Latent Dirichlet Allocation (LDA)
Non-negative Matrix Factorization (NMF)
Latent Semantic Analysis (LSA)
Clustering:
K-Means / GMM on TF-IDF or embeddings (e.g., BERT)
Hierarchical Clustering on document embeddings
Dimensionality Reduction:
t-SNE / UMAP on word or sentence embeddings
Autoencoders (on sentence/document embeddings)
Anomaly Detection:
Isolation Forest on text vector features
Autoencoders detecting rare/abnormal phrasing
One-Class SVM (on document vectors)