> For the complete documentation index, see [llms.txt](https://smartpredict.gitbook.io/smartpredict-ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://smartpredict.gitbook.io/smartpredict-ai/module-reference/core-modules/8.-machine-learning-algorithms/ml-modules-in-smartpredict.md).

# ML modules in SmartPredict

To help you complete your Machine Learning projects from end to end, *SmartPredict studio* provides the state-of-the-art  of ML algorithms.

The ML modules are parameterizable at will and are insured to cover all the granularity of your projects. 

{% hint style="info" %}
Both Classifiers and Regressors  belong to the family of tools used for [Supervised Learning](https://scikit-learn.org/stable/supervised_learning.html).                        This [article](https://medium.com/quick-code/regression-versus-classification-machine-learning-whats-the-difference-345c56dd15f7) resumes well the main differences between the two as well as their uses.
{% endhint %}

SmartPredict' s toolbox contains various  ML modules, made of regressors and classifiers from decision tree regressor to XGBoost Regressor. 

* **Decision Tree Regressor**
* **KNeighbors Classifiers** 
* **KNeighbors Regressor** 
* **Linear Regressor** 
* **Logistic Regressor** 
* **MLP Regressor**
* **Naive Bayes**
* **Random Forest Classifier** 
* **Random Forest Regressor** 
* **Support Vector Classifier** 
* **Support Vector Regressor**
* **XGBoost Classifier** 
* **XGBoost Regressor**

{% hint style="info" %}
More information on supervised learning such as details on the Nearest Neighbors or Support Vector machines  are well-documented in the official [Sci-kit learn documentation](https://scikit-learn.org/stable/supervised_learning.html) . 
{% endhint %}