# Modeling algorithms > Modeling algorithms - Provides a list of the supervised and unsupervised modeling algorithms > DataRobot supports. This Markdown file sits beside the HTML page at the same path (with a `.md` suffix). It summarizes the topic and lists links for tools and LLM context. Companion generated at `2026-05-01T23:10:48.115562+00:00` (UTC). ## Primary page - [Modeling algorithms](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/model-list.html): Full documentation for this topic (HTML). ## Sections on this page - [Pre-processing tasks](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/model-list.html#pre-processing): In-page section heading. - [Categorical](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/model-list.html#categorical): In-page section heading. - [Numerical](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/model-list.html#numerical): In-page section heading. - [Geospatial](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/model-list.html#geospatial): In-page section heading. - [Images](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/model-list.html#images): In-page section heading. - [Text models](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/model-list.html#text-models): In-page section heading. - [Generalized Linear Models](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/model-list.html#generalized-linear-models): In-page section heading. - [Linear or additive models](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/model-list.html#linear-or-additive-models): In-page section heading. - [Generalized Linear Models](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/model-list.html#generalized-linear-models_1): In-page section heading. - [Support Vector Machines](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/model-list.html#support-vector-machines): In-page section heading. - [Generalized Additive Models](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/model-list.html#generalized-additive-models): In-page section heading. - [Tree-based models](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/model-list.html#tree-based-models): In-page section heading. - [Deep learning and foundational models](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/model-list.html#deep-learning-and-foundational-models): In-page section heading. - [Time series-specific models](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/model-list.html#time-series-specific-models): In-page section heading. - [Unsupervised models](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/model-list.html#unsupervised-models): In-page section heading. - [Anomaly detection models](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/model-list.html#anomaly-detection-models): In-page section heading. - [Clustering models](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/model-list.html#clustering-models): In-page section heading. - [Other model types](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/model-list.html#other-model-types): In-page section heading. ## Related documentation - [Reference documentation](https://docs.datarobot.com/en/docs/reference/index.html): Linked from this page. - [Predictive AI reference](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/index.html): Linked from this page. - [blueprint](https://docs.datarobot.com/en/docs/api/reference/public-api/blueprints.html): Linked from this page. - [model repository](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/build-basic/repository.html): Linked from this page. - [Composable ML](https://docs.datarobot.com/en/docs/classic-ui/modeling/special-workflows/cml/index.html): Linked from this page. ## Documentation content # Modeling algorithms DataRobot supports a comprehensive library of pre- and post-processing (modeling) steps, which combine to make up the model [blueprint](https://docs.datarobot.com/en/docs/api/reference/public-api/blueprints.html). Which are run or available in the [model repository](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/build-basic/repository.html) is dependent on the dataset. The comprehensive combination of pre- and post-processing steps allows DataRobot to confidently create a Leaderboard of your best modeling options. Some examples of the modeling flexibility include logistic regression with and without PCA as a pre-processor or random forests with and without a greedy search for interaction terms. The implication of this is that for every model in the list below, DataRobot likely runs two-to-five times, each with a different pre-processing and/or variable selection. The following sections list the relevant algorithms: - Pre-processing - Linear or additive models - Tree-based models - Deep learning and foundational models - Time series-specific models - Unsupervised models - Other model types ## Pre-processing tasks #### Categorical - Buhlman credibility estimates for high cardinality features - Categorical embedding - Category count - One-hot encoding - Ordinal encoding of categorical variables - Univariate credibility estimates with L2 - Efficient, sparse one-hot encoding for extremely high cardinality categorical variables #### Numerical - Binning of numerical variables - Constant splines - Missing values imputed - Numeric data cleansing - Partial Principal Components Analysis - Truncated Singular Values Decomposition - Normalizer #### Geospatial - Geospatial Location Converter - Spatial Neighborhood Featurizer #### Images - Greyscale Downscaled Image Featurizer - No Post Processing - OpenCV detect largest rectangle - OpenCV image featurizer - Pre-trained multi-level global average pooling image featurizer #### Text models - Character / word n-grams - Pretrained byte-pair encoders (best of both words for char-grams and n-grams) - Stopword removal - TF-IDF scaling (optional sublinear scaling and binormal separation scaling) - Hashing vectorizers for big data - Cosine similarity between pairs of text columns (on datasets with 2+ text columns) - Support for all languages, including English, Japanese, Chinese, Korean, French, Spanish, Chinese, Portuguese, Arabic, Ukrainian, Klingon, Elvish, Esperanto, etc. - Unsupervised Fasttext models - Linear n-gram models (character/word n-grams + TF-IDF + penalized linear/logistic regression) - SVD n-gram models (n-grams + TF-IDF + SVD) - Naive Bayes weighted SVM - TinyBERT / Roberta/ MiniLM embedding models - Text CNNs #### Generalized Linear Models - NA imputation (methods for missing at random and missing not at random), standardization, ridit transform - Search for best transformations - Efficient, sparse one-hot encoding for extremely high cardinality categorical variables ## Linear or additive models #### Generalized Linear Models - Penalty: L1 (Lasso), L2 (Ridge), ElasticNet, None (Logistic Regression) - Distributions: Binomial, Gaussian, Poisson, Tweedie, Gamma, Huber - Special Cases: 2-stage model (Binomial + Gaussian) for zero-inflated regression #### Support Vector Machines - Penalty: L1 (Lasso), L2 (Ridge), ElasticNet, None - Kernel: Linear, Nyström RFB, RBF - liblinear and libsvm #### Generalized Additive Models - GAM - GA2M ## Tree-based models - Decision Tree (or CART) - Random Forest - ExtraTrees (or Extremely Randomized Forests) - Gradient Boosted Trees (or GBM— Binomial, Gaussian, Poisson, Tweedie, Gamma, Huber) - Extreme Gradient Boosted Trees (or XGBoost— Binomial, Gaussian, Poisson) - LightGBM - AdaBoost - RuleFit ## Deep learning and foundational models - Keras MLPs with residual connections, adaptive learning rates and adaptive batch sizes - Keras self-normalizing MLPs with residual connections - Keras neural architecture search MLPs using hyperband - DeepCTR - Pretrained CNNs for images using foundational models (especially EfficientNet) - Pretrained + fine-tuned CNNs for images - Image augmentation - Pretrained TinyBERT models for text - Keras Text CNNs - Fastext models for text ## Time series-specific models - LSTMs - DeepAR models - AutoArima - ETS, aka exponential smoothing - TBATS - Prophet ## Unsupervised models #### Anomaly detection models - Isolation Forest - Local Outlier Factor - One Class SVM - Double Median Absolute Deviation - Mahalanobis Distance - Anomaly Detection Blenders - Keras Deep Autoencoder - Keras Deep Variational Autoencoder #### Clustering models - Kmeans - HDBScan ## Other model types - Eureqa (proprietary genetic algorithm for symbolic regression) - K-Nearest Neighbors (three distances) - Partial-least squares (used for blenders) - Isotonic Regression (used for calibrating predictions from other models) Click a [blueprint](https://docs.datarobot.com/en/docs/api/reference/public-api/blueprints.html) node to access full model documentation. Using [Composable ML](https://docs.datarobot.com/en/docs/classic-ui/modeling/special-workflows/cml/index.html), you can build models that best suit your needs using built-in tasks and custom Python/R code.