Standard Models

These models are available to use instantly on IntegrateFL

These standard models are defined using JSON configuration files during session creation. See here for examples of data configuration and model configuration files. See here for a glossary of model parameters that can be used in the configuration files.

Generalized Linear Models (iai_glm)

This model class supports a variety of regression models. Examples include linear regression, logistic regression, poisson regression, gamma regression and inverse Gaussian regression models. We also support regularizing the model coefficients with the elastic net penalty.

Examples of use cases include [1]:

• Agriculture / weather modeling: number of rain events per year, amount of rainfall per event, total rainfall per year

• Risk modeling / insurance policy pricing: number of claim events / policyholder per year, cost per event, total cost per policyholder per year

• Predictive maintenance: number of production interruption events per year, duration of interruption, total interruption time per year

Feed Forward Neural Nets (iai_ffnet)

Feedforward neural nets are a type of neural net in which information flows the nodes in a single direction.

Examples of use cases include:

• Classification tasks like image recognition or churn conversion prediction.

• Regression tasks like forecasting revenues and expenses, or determining the relationship between drug dosage and blood pressure

References [1] https://scikit-learn.org/stable/modules/linear_model.html#generalized-linear-regression