---
warning: DO NOT CHANGE THIS MANUALLY, THIS IS GENERATED BY https://github.com/duckdb/community-extensions repository, check README there
title: mlpack
excerpt: |
DuckDB Community Extensions
Connecting duckdb to the mlpack C++ machine learning library
extension:
name: mlpack
description: Connecting duckdb to the mlpack C++ machine learning library
version: 0.0.7
language: C++
build: cmake
license: MIT
excluded_platforms: "windows_amd64_mingw;windows_amd64;wasm_mvp;wasm_eh;wasm_threads;osx_amd64"
requires_toolchains: "fortran;omp"
maintainers:
- eddelbuettel
repo:
github: eddelbuettel/duckdb-mlpack
andium: 8437d78423e3cfc65f9226606908b301c2314710
ref: ed1891ddf415491a2c0c24009d7a7b9aed32bead
docs:
hello_world: |
-- Perform adaBoost (using weak learner 'Perceptron' by default)
-- Read 'features' into 'X', 'labels' into 'Y', use optional parameters
-- from 'Z', and prepare model storage in 'M'
CREATE TABLE X AS SELECT * FROM read_csv("https://eddelbuettel.github.io/duckdb-mlpack/data/iris.csv");
CREATE TABLE Y AS SELECT * FROM read_csv("https://eddelbuettel.github.io/duckdb-mlpack/data/iris_labels.csv");
CREATE TABLE Z (name VARCHAR, value VARCHAR);
INSERT INTO Z VALUES ('iterations', '50'), ('tolerance', '1e-7');
CREATE TABLE M (key VARCHAR, json VARCHAR);
-- Train model for 'Y' on 'X' using parameters 'Z', store in 'M'
CREATE TEMP TABLE A AS SELECT * FROM mlpack_adaboost_train("X", "Y", "Z", "M");
-- Count by predicted group
SELECT COUNT(*) as n, predicted FROM A GROUP BY predicted;
-- Model 'M' can be used to predict
CREATE TABLE N (x1 DOUBLE, x2 DOUBLE, x3 DOUBLE, x4 DOUBLE);
-- inserting approximate column mean values
INSERT INTO N VALUES (5.843, 3.054, 3.759, 1.199);
-- inserting approximate column mean values, min values, max values
INSERT INTO N VALUES (5.843, 3.054, 3.759, 1.199), (4.3, 2.0, 1.0, 0.1), (7.9, 4.4, 6.9, 2.5);
-- and this predict one element each
SELECT * FROM mlpack_adaboost_pred("N", "M");
extended_description: |
### Supervised Learning
The mlpack extension allows to fit (or train) and predict (or classify) from the models implemented, currently adaBoost, Bayesian linear regression, decision tree, LARS, linear regression,
logistic regression, naive Bayes, random forests as well as softmax regression.
The format is the same for these methods: four tables, say, "X", "Y", "Z" and "M" provide input for, respectively, features "X", labels "Y", optional parameters varying by model in "Z" as well as an output table "M" for the JSON-serialized model.
For all methods, following a model fit (or training), a prediction (or classification) can be made using "M" and new predictor values "N" as shown in the example.
All these "fit" (or "train") methods take four parameter tables, all "predict" methods take two.
### Unsupervised Learning
A kMeans clustering method is also available.
It uses three tables for data, parameters and results.
### General Information
A pair of parameters "mlpack_verbose" (to show additional data) and "mlpack_silent" (to suppress display of minimal summaries) can also be set.
The implementation is wraps several supervised and unsupervised machine learning methods, and provides Linux and macOS builds.
More methods, options or parameters can be added quite easily.
For more, please see the [repo](https://github.com/eddelbuettel/duckdb-mlpack).
extension_star_count: 21
extension_star_count_pretty: 21
extension_download_count: 984
extension_download_count_pretty: 984
image: '/images/community_extensions/social_preview/preview_community_extension_mlpack.png'
layout: community_extension_doc
---
### Compatibility Matrix
This table covers all DuckDB releases that have not reached [end-of-life](https://duckdb.org/release_calendar), including LTS releases, and shows the 2 most recent patch releases of each. Older patch releases are not listed, but the extension may be available for them as well.
| Platform | 1.4.4 | 1.4.5 | 1.5.5 | 1.5.6 | 2.0.0 |
| :--- | :---: | :---: | :---: | :---: | :---: |
| Linux AMD64 | ✅ | ❌ | ✅ | ✅ | ✅ |
| Linux ARM64 | ✅ | ❌ | ✅ | ✅ | ✅ |
| macOS AMD64 | ✅ | ❌ | ✅ | ✅ | ❌ |
| macOS ARM64 | ✅ | ❌ | ✅ | ✅ | ✅ |
| Windows AMD64 | ❌ | ❌ | ❌ | ❌ | ❌ |
| Windows ARM64 | ❌ | ❌ | ❌ | ❌ | ❌ |
| WebAssembly | ❌ | ❌ | ❌ | ❌ | ❌ |
### Installing and Loading
```sql
INSTALL {{ page.extension.name }} FROM community;
LOAD {{ page.extension.name }};
```
{% if page.docs.hello_world %}
### Example
```sql
{{ page.docs.hello_world }}```
{% endif %}
{% if page.docs.extended_description %}
### About {{ page.extension.name }}
{{ page.docs.extended_description }}
{% endif %}
### Added Functions
| function_name | function_type | description | comment | examples |
|----------------------------------------|---------------|---------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------|----------|
| mlpack_adaboost_train | table | use adaboost to train and store a model | parameters 'iterations', 'tolerance', 'perceptronIter' and 'silent' | NULL |
| mlpack_adaboost_pred | table | predict classification using stored adaboost model | NULL | NULL |
| mlpack_bayesian_linear_regression_fit | table | fit and store Bayesian linear regression model | parameters 'center', 'scale' and 'silent' | NULL |
| mlpack_bayesian_linear_regression_pred | table | predict using stored Bayesian linear regression model | NULL | NULL |
| mlpack_decision_tree_train | table | fit and store decision tree classification | parameters 'numClasses', 'minimumLeafSize', 'minimumGainSplit', 'maximumDepth', and 'silent' | NULL |
| mlpack_decision_tree_pred | table | predict classification using stored decision tree model | NULL | NULL |
| mlpack_lars_fit | table | fit and store LARS regression model | parameters 'lambda1', 'lambda2', 'use_cholesky', 'no_intercept', 'no_normalize' and 'silent' | NULL |
| mlpack_lars_pred | table | predict using stored LARS regression model | NULL | NULL |
| mlpack_linear_regression_fit | table | fit and store linear regression model | parameters 'lambda', 'intercept' and 'silent' | NULL |
| mlpack_linear_regression_pred | table | predict using stored linear regression model | NULL | NULL |
| mlpack_linear_svm_train | table | fit and store linear SVM classification | parameters 'numClasses', 'lambda', 'delta', 'fitIntercept', 'standardize', 'maxIterations', 'tolerance', and 'silent' | NULL |
| mlpack_linear_svm_pred | table | predict classification using stored linear SVM model | NULL | NULL |
| mlpack_logistic_regression_fit | table | fit and store logistic regression model | parameters 'lambda', 'intercept' and 'silent' | NULL |
| mlpack_logistic_regression_pred | table | predict classification using stored logistic regression model | NULL | NULL |
| mlpack_naive_bayes_train | table | fit and store naive Bayes classification | parameters 'numClasses', 'incremental_variance', and 'silent' | NULL |
| mlpack_naive_bayes_pred | table | predict classification using stored naive Bayes model | NULL | NULL |
| mlpack_random_forest_train | table | use random forest to train and store a model | parameters 'nclasses', 'ntrees', 'seed', 'threads' and 'silent' | NULL |
| mlpack_random_forest_pred | table | predict classification using stored random forest model | NULL | NULL |
| mlpack_softmax_regression_fit | table | fit and store softmax regression classification | parameters 'numClasses', 'lambda', 'fitIntercept', 'silent' | NULL |
| mlpack_softmax_regression_pred | table | predict classification using softmax regression model | NULL | NULL |
| mlpack_kmeans | table | use kmeans unsupervised clustering | parameters 'clusters', and 'iterations' | NULL |
| mlpack_mlpack_version | scalar | returns the version string for the mlpack version used | NULL | NULL |
| mlpack_armadillo_version | scalar | returns the version string for the armadillo version used | NULL | NULL |
### Overloaded Functions
This extension does not add any function overloads.
### Added Types
This extension does not add any types.
### Added Settings
| name | description | input_type | scope | aliases | typed_value |
|----------------|-------------------------------------------------------------|------------|--------|---------|-------------|
| mlpack_silent | Toggle whether to operate in silent mode, default is false | BOOLEAN | GLOBAL | [] | false |
| mlpack_verbose | Toggle whether to operate in verbose mode, default is false | BOOLEAN | GLOBAL | [] | false |
| temp_directory | Set the directory to which temporary files are written. | VARCHAR | GLOBAL | [] | post.db.tmp |