# 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.&#x20;

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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.&#x20;

* **Decision Tree Regressor**
* **KNeighbors Classifiers**&#x20;
* **KNeighbors Regressor**&#x20;
* **Linear Regressor**&#x20;
* **Logistic Regressor**&#x20;
* **MLP Regressor**
* **Naive Bayes**
* **Random Forest Classifier**&#x20;
* **Random Forest Regressor**&#x20;
* **Support Vector Classifier**&#x20;
* **Support Vector Regressor**
* **XGBoost Classifier**&#x20;
* **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) .&#x20;
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