Compatibility Table

AI Framework and Model Types

In this table, we list the supported AI framework and algorithms.

Framework	Version	Algorithm	Model Type
scikit-learn	1.4.0	Binary Classification	Logistic Regression
			Decision Tree
			Random Forest
			Gradient Boosting Classifier
			Perceptron
			Bagging Classifier
			Linear Support Vector Classifier
		Multiclass Classification	Logistic Regression
			Decision Tree
			Random Forest
			Gradient Boosting Classifier
			Perceptron
			Bagging Classifier
			Linear Support Vector Classifier
		Regression	Linear Regression
			Extra Tree Regressor
			Gradient Boosting Regressor
			Random Forest Regression
Tensorflow	2.13.0	Binary Classification	Keras Sequential
		Multiclass Classification	Keras Sequential
		Regression	Keras Sequential
PyTorch	>2.0	Binary Classification	PyTorch model
		Multiclass Classification	PyTorch model
		Regression	PyTorch model
XGBoost	2.0.3	Binary Classification	XGB Classifier
			XGB Booster
		Multiclass Classifcation	XGB Classifier
		Regression	XGB Regressor
LightGBM	4.3.0	Binary Classification	LGBM Classifier

Data Serialisers

Library	Version
pickle	Version is based on the pickle installed in your environment
joblib	1.4.2

Info

If your datasets and models are serialised using other version, please modify your environment accordingly.