> ## Documentation Index > Fetch the complete documentation index at: https://docs.ocient.com/llms.txt > Use this file to discover all available pages before exploring further. # Machine Learning Model Functions > Reference for OcientML model functions used in SQL queries to invoke trained machine learning models for prediction, scoring, and feature engineering tasks. export const OcientML = "OcientML™"; {OcientML} functionality supports these SQL functions for machine learning models. The scope of a machine learning model is the schema. ## Supported Machine Learning Models Supported machine learning models and reference material are divided into these categories. For functions to help organize data before training a model, see [Data Preparation](/data-preparation). #### Regression Models * [Simple Linear Regression](/regression-models#simple-linear-regression) * [Multiple Linear Regression](/regression-models#multiple-linear-regression) * [Vector Autoregression](/regression-models#vector-autoregression) * [Polynomial Regression](/regression-models#polynomial-regression) * [Linear Combination Regression](/regression-models#linear-combination-regression) * [Nonlinear Regression](/regression-models#nonlinear-regression) * [Gradient Boosted Trees](/regression-models#gradient-boosted-trees) * [Regression Tree](/regression-models#regression-tree) #### Classification Models * [K Nearest Neighbor Classification](/classification-models#k-nearest-neighbors-classification) * [Naive Bayes Classification](/classification-models#naive-bayes-classification) * [Decision Tree](/classification-models#decision-tree) * [Random Forest](/classification-models#random-forest) * [Logistic Regression](/classification-models#logistic-regression) * [Support Vector Machine](/classification-models#support-vector-machine) * [Gradient Boosted Trees](/classification-models#execute-the-model-6) #### Clustering and Dimension Reduction Models * [Principal Component Analysis](/clustering-and-dimension-reduction-models#principal-component-analysis) * [K-Means Clustering](/clustering-and-dimension-reduction-models#k-means-clustering) * [Gaussian Mixture](/clustering-and-dimension-reduction-models#gaussian-mixture) * [Linear Discriminant Analysis](/clustering-and-dimension-reduction-models#linear-discriminant-analysis) #### Ensemble Models * [Bagging](/ensemble-models#bagging) * [Boosting](/ensemble-models#boosting) * [Stacking](/ensemble-models#stacking) #### Other Models * [Association Rules](/other-models#association-rules) * [Multi-Layer Perceptron (MLP)](/other-models#multi-layer-perceptron-mlp) * [Undersampling](/other-models#undersampling) For a view of the full list of model options, see [Machine Learning Model Options](/machine-learning-model-options). ## Execute a Query Using a Machine Learning Model To create a machine learning model and manage the model, see [Machine Learning Models](/machine-learning-models) for the corresponding syntax. After you create the model, you can execute a query using the model with this syntax. **Syntax** ```sql SQL theme={null} SELECT model_name ( expression [, ... ] ) FROM table_reference ``` | **Parameter** | **Data Type** | **Description** | | - | - | - | | `model_name` | identifier | The name of a machine learning model created using a `CREATE MODEL` SQL statement. The model name must be a valid identifier and reference an existing trained model. | | `expression` | identifier
string
numeric
| One or more expressions that serve as input features for the machine learning model evaluation. These expressions must match the expected input schema that the model was trained on.

Expressions can be any combination of literal values, column names, arithmetic expressions, and function invocations, with parentheses for grouping. | | `table_reference` | identifier | The name of a table, view, or subquery that provides the data for model evaluation. The table must contain columns or computed expressions that match the expected input features of the model. | **Example** Create a table with data for the model. ```sql SQL theme={null} CREATE TABLE mldemo.mlr AS (SELECT a.c1 AS x1, b.c1 AS x2, 1 + 2*a.c1 + 3*b.c1 AS y FROM sys.dummy10 a, sys.dummy10 b); Modified 100 rows ``` Create a multiple linear regression model based on the data. ```sql SQL theme={null} CREATE MLMODEL mlr_model TYPE MULTIPLE LINEAR REGRESSION ON (SELECT * FROM mldemo.mlr) options('metrics' -> 'true'); Modified 0 rows ``` Execute a `SELECT` query against the multiple linear regression to see the actual and predicted values. Limit the result set to 10 rows. ```sql SQL theme={null} SELECT x1, x2, y AS actual, mlr_model(x1, x2) AS predicted FROM mldemo.mlr LIMIT 10; ``` *Output* ```none Text theme={null} x1 x2 actual predicted ---------------------------------------------------------------- 6 1 16 15.999999999999975 6 2 19 18.999999999999975 6 3 22 21.999999999999975 6 4 25 24.999999999999975 6 5 28 27.999999999999975 6 6 31 30.999999999999975 6 7 34 33.99999999999997 6 8 37 36.99999999999997 6 9 40 39.99999999999997 6 10 43 42.99999999999997 Fetched 10 rows ``` ## Machine Learning Meta Functions Machine Learning meta functions provide additional ways to invoke a trained model. Unlike calling a model directly by name, meta functions wrap the model invocation and control how the model produces its output. ### predict The `predict` meta function behaves the same as invoking the model directly as a function. The function returns the value predicted by the specified model for the input features. **Syntax** ```sql SQL theme={null} SELECT predict( model_name( [ hyperargument [, ...] ] ), feature [, ...] ) FROM table_reference ``` | **Parameter** | **Data Type** | **Description** | | - | - | - | | `model_name` | identifier | The name of a trained machine learning model. | | `hyperargument` | varies | Optional model-specific arguments that control how the model makes predictions. For example, the component index in principal component analysis, or a voting flag for ensemble models. | | `feature` | varies | One or more input feature expressions matching the schema the model was trained on. | **Example** ```sql SQL theme={null} SELECT predict(my_model(), x1, x2) FROM my_table; ``` Because many models do not require hyperarguments, the hyperargument list and its surrounding parentheses are optional. ```sql SQL theme={null} SELECT predict(my_model, x1, x2) FROM my_table; ``` ### predict\_probabilities The `predict_probabilities` meta function returns a tuple of label-score pairs for each possible class the model can predict. The model assigns each label a score determined by the input features. Then, the model sorts the pairs in decreasing order of score, with ties broken by decreasing label value. This function is supported only for these model types: * Decision Tree * Random Forest * Logistic Regression * Support Vector Machine * Gaussian Discriminant Analysis * Gaussian Mixture Model * MLP (only when trained with softmax output for classification) All other model types reject the `predict_probabilities` function, including all regression variants, Naive Bayes, Linear Discriminant Analysis, Principal Component Analysis, ensemble models (Bagging, Stacking), K-Means, Power Iteration Clustering, K Nearest Neighbors, and anomaly detection models. Undersampling models do not support either meta function because they produce output tables rather than row-level predictions. **Syntax** ```sql SQL theme={null} SELECT predict_probabilities( model_name( [ hyperargument [, ...] ] ), feature [, ...] ) FROM table_reference ``` | **Parameter** | **Data Type** | **Description** | | - | - | - | | `model_name` | identifier | The name of a trained machine learning model. | | `hyperargument` | varies | Optional model-specific arguments that control how the model makes predictions. | | `feature` | varies | One or more input feature expressions matching the schema the model was trained on. | **Example** ```sql SQL theme={null} SELECT predict_probabilities(my_classifier(), x1, x2, x3) FROM my_table; ``` ## Related Links [Machine Learning in Ocient](/machine-learning-in-ocient) [Machine Learning Models](/machine-learning-models)