Supercharge your predictive AI projects.
Try Gooder AI: easy, fast, free
This webpage has everything you need to get started hands-on with Gooder AI and realize the business potential of your machine learning models.
First, if you're not familiar with Gooder AI, ML valuation, and the advantage of planning machine learning deployment by viewing its potential business value, then begin with the introductory videos above.
Second, get started using Gooder AI by watching the series of short hands-on, how-to videos below.
Videos
Watch the first hands-on, how-to video:
Watch the full playlist of how-to videos, which includes:
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Supercharging your predictive AI projects
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How to valuate XGBoost and scikit-learn models
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Customizing #MLvaluation for each predictive AI project
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A breakthrough for predictive AI: driving decisions with "expected value"
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A crystal-clear business view into predictive AI projects
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Optimizing the decision threshold for predictive AI deployment
Supporting links and files
Open the Gooder AI product: app.gooder.ai
Download the standard starter configuration file: docs.gooder.ai
Documentation: Overview of standard earnings curves/metrics – this PDF summarizes the earnings metrics available within the starter config
Documentation: Gooder AI in Python for XGBoost and scikit-learn models – this includes the sample Jupyter Notebook for fraud detection (at the bottom)
Datasets and associated config files used in the how-to videos: