# Metis Tuner¶

Metis offers several benefits over other tuning algorithms. While most tools only predict the optimal configuration, Metis gives you two outputs, a prediction for the optimal configuration and a suggestion for the next trial. No more guess work!

While most tools assume training datasets do not have noisy data, Metis actually tells you if you need to resample a particular hyper-parameter.

While most tools have problems of being exploitation-heavy, Metis’ search strategy balances exploration, exploitation, and (optional) resampling.

Metis belongs to the class of sequential model-based optimization (SMBO) algorithms and it is based on the Bayesian Optimization framework. To model the parameter-vs-performance space, Metis uses both a Gaussian Process and GMM. Since each trial can impose a high time cost, Metis heavily trades inference computations with naive trials. At each iteration, Metis does two tasks:

It finds the global optimal point in the Gaussian Process space. This point represents the optimal configuration.

It identifies the next hyper-parameter candidate. This is achieved by inferring the potential information gain of exploration, exploitation, and resampling.

Note that the only acceptable types within the search space are `quniform`

, `uniform`

, `randint`

, and numerical `choice`

.

More details can be found in our paper.

## Usage¶

### classArgs requirements¶

**optimize_mode**(*‘maximize’ or ‘minimize’, optional, default = ‘maximize’*) - If ‘maximize’, the tuner will try to maximize metrics. If ‘minimize’, the tuner will try to minimize metrics.

### Example Configuration¶

```
# config.yml
tuner:
name: MetisTuner
classArgs:
optimize_mode: maximize
```