Machine Learning Toolkit on i2G offers the most advanced predictive algorithms with a fully controllable parameter set-up in an intuitive graphic user interface. The toolkit supports multi-zone and multi-well selection to optimize petrophysical tasks with:

- Robust algorithms designed by geoscientists and machine learning experts
- Reproducible results by allowing seed number input
- Flexibility in conﬁguring model inputs and architectures
- Easy-to-use and adjustable workflow interface
- Availability of different model validation metrics

**Regression Model**

The built model can used to predict quantitative petrophysical/geochemical/geomechanical properties as well as generate synthetic output for missing data or bad-hole intervals

Multi perceptron algorithm enables building multi-layer neural network with non-linear activation functions to solve complex geological settings. Here is the list of supporting regression methods:

- Decision Tree Regression
- Huber Regression
- Lasso Regression
- Linear Regression
- Neural Network Regression
- Random Forest Regression
- SVM Regression
- Xgboost Regression

**Classification Model**

The built model can used to predict discrete properties such as lithofacies, depofacies, flow units or rock types. The module offers diversiﬁed ways to assess the model: loss and accuracy crossplots as well as confusion matrix. Similar to regression model, user has an ability to perform data ﬁltering and input ranking.

Here is the list of supporting regression methods:

- Decision Tree Classification
- KNN Classification
- Logistic Classification
- Neural Network Classification
- Random Forest Classification

**Self-organizing Map (SOM)**

Self-organizing Map is a unique tool for facies classification by offering both supervised and unsupervised learning algorithms.

**Non-linear regression**

This is a nonlinear approach to model the relationship between the input curves and the target curve. The relationships are modeled using nonlinear predictor functions, so called nonlinear models, whose unknown model parameters are estimated from the data. The nonlinear relationship function is defined by the user, where the variables x1, x2, … correspond to the input curves.