Google Code: Prediction API Gets Tunable Predictive Models

Google Code team today as of August 4 added a new feature: "the ability to adjust models to get better performance" to the Prediction API."Historically, getting the right predictive model has required detailed knowledge of algorithmic behavior and experience with similar datasets, and a lot of guess-and-check. With the Prediction API, we ask you what […]

Google Code team today as of August 4 added a new feature: "the ability to adjust models to get better performance" to the Prediction API.

"Historically, getting the right predictive model has required detailed knowledge of algorithmic behavior and experience with similar datasets, and a lot of guess-and-check. With the Prediction API, we ask you what behavior you want to see, and search across many algorithms to find the best-matching one," wrote Scott Knaster.

How it works:

  1. Upload data to Google Storage for Developers.
  2. Ask the Prediction API to find a great predictive model.
  3. [new] Examine more detailed statistics about your model's performance, including more training metadata and better accuracy statistics through a confusion matrix.
  4. Improve performance.
    1. Give your model more samples to learn from.
    2. Add in more information (see these samples).
    3. [new] Show the API what data is most important (categorical data only).

For those of you ready to get started, jump in through newly updated code samples.

[Source: Code blog]