WEBINAR / EVENT

Using Trained Machine Learning Predictors in Gurobi

Webinar: Using Trained Machine Learning Predictors in Gurobi.

January 25 | February 2 | February 7 2023

WEBINAR / EVENT

Using Trained Machine Learning Predictors in Gurobi

Webinar: Using Trained Machine Learning Predictors in Gurobi.

January 25 | February 2 | February 7 2023

WEBINAR / EVENT

Using Trained Machine Learning Predictors in Gurobi

Webinar: Using Trained Machine Learning Predictors in Gurobi.

January 25 | February 2 | February 7 2023

Event Recap

Machine learning has become a prevalent tool to provide predictive models in many applications, in this webinar relationships between variables of an optimization model in Gurobi will be discussed.

In recent years, machine learning has become a prevalent tool to provide predictive models in many applications. In this talk, we are interested in using such predictors to model relationships between variables of an optimization model in Gurobi. For example, a regression model may predict the demand of certain products as a function of their prices and marketing budgets among other features. We are interested in being able to build optimization models that embed the regression so that the inputs of the regression are decision variables, and the predicted demand can be satisfied.

We propose a python package that aims at making it easy to insert regression models trained by popular frameworks (e.g., scikit-learn, Keras, PyTorch) into a Gurobi model. The regression model may be a linear or logistic regression, a neural network, or based on decision trees. 

 

Presented Materials:

Download the presentation, here.

The repository

Documentation

Price optimization example

Event Recap

Machine learning has become a prevalent tool to provide predictive models in many applications, in this webinar relationships between variables of an optimization model in Gurobi will be discussed.

In recent years, machine learning has become a prevalent tool to provide predictive models in many applications. In this talk, we are interested in using such predictors to model relationships between variables of an optimization model in Gurobi. For example, a regression model may predict the demand of certain products as a function of their prices and marketing budgets among other features. We are interested in being able to build optimization models that embed the regression so that the inputs of the regression are decision variables, and the predicted demand can be satisfied.

We propose a python package that aims at making it easy to insert regression models trained by popular frameworks (e.g., scikit-learn, Keras, PyTorch) into a Gurobi model. The regression model may be a linear or logistic regression, a neural network, or based on decision trees. 

 

Presented Materials:

Download the presentation, here.

The repository

Documentation

Price optimization example

Event Recap

Machine learning has become a prevalent tool to provide predictive models in many applications, in this webinar relationships between variables of an optimization model in Gurobi will be discussed.

In recent years, machine learning has become a prevalent tool to provide predictive models in many applications. In this talk, we are interested in using such predictors to model relationships between variables of an optimization model in Gurobi. For example, a regression model may predict the demand of certain products as a function of their prices and marketing budgets among other features. We are interested in being able to build optimization models that embed the regression so that the inputs of the regression are decision variables, and the predicted demand can be satisfied.

We propose a python package that aims at making it easy to insert regression models trained by popular frameworks (e.g., scikit-learn, Keras, PyTorch) into a Gurobi model. The regression model may be a linear or logistic regression, a neural network, or based on decision trees. 

 

Presented Materials:

Download the presentation, here.

The repository

Documentation

Price optimization example

Speakers

Meet Your Expert Speaker

Learn from the best in the industry, bringing years of experience and groundbreaking insights to the forefront of AI personalization.

Speakers

Meet Your Expert Speaker

Learn from the best in the industry, bringing years of experience and groundbreaking insights to the forefront of AI personalization.

Speakers

Meet Your Expert Speaker

Learn from the best in the industry, bringing years of experience and groundbreaking insights to the forefront of AI personalization.