| --- |
| title: ML Tuning and Evaluation |
| --- |
| |
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| |
| PredictionIO's evaluation module allows you to streamline the process of |
| testing lots of knobs in engine parameters and deploy the best one out |
| of it using statisically sound cross-validation methods. |
| |
| There are two key components: |
| |
| ### Engine |
| |
| It is our evaluation target. During evaluation, in addition to |
| the *train* and *deploy* mode we describe in earlier sections, |
| the engine also generates a list of testing data points. These data |
| points are a sequence of *Query* and *Actual Result* tuples. *Queries* are |
| sent to the engine and the engine responds with a *Predicted Result*, |
| in the same way as how the engine serves a query. |
| |
| ### Evaluator |
| |
| The evaluator joins the sequence of *Query*, *Predicted Result*, and *Actual Result* |
| together and evaluates the quality of the engine. |
| PredictionIO enables you to implement any metric with just a few lines of code. |
| |
|  |
| |
| We will discuss various aspects of evaluation with PredictionIO. |
| |
| - [Hyperparameter Tuning](/evaluation/paramtuning/) - it is an end-to-end example |
| of using PredictionIO evaluation module to select and deploy the best engine |
| parameter. |
| - [Evaluation Dashboard](/evaluation/evaluationdashboard/) - it is the dashboard |
| where you can see a detailed breakdown of all previous evaluations. |
| - [Choosing Evaluation Metrics](/evaluation/metricchoose/) - we cover some basic |
| machine learning metrics |
| - [Bulding Evaluation Metrics](/evaluation/metricbuild/) - we illustrate how to |
| implement a custom metric with as few as one line of code (plus some |
| boilerplates). |
