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Production-ready hyperparameter optimization for Elixir with high Optuna parity. Leverages BEAM fault tolerance, real-time dashboards, and native distributed computing.

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# lib/scout/pruner/ - Pruning Strategies
## Overview
Early stopping algorithms to terminate unpromising trials early.
## Available Pruners
- `successive_halving.ex` - Successive Halving Algorithm (SHA) for aggressive early stopping
- `median.ex` - Prune trials below median performance at checkpoints
## Successive Halving
- Implements SHA with configurable rungs
- Progressively eliminates bottom performers
- Efficient for large search spaces
- Foundation for Hyperband algorithm
## Interface
```elixir
def should_prune?(study, trial, observation) do
# Returns {:prune, reason} or :continue
end
```
## Usage in Studies
```elixir
%{
pruner: Scout.Pruner.SuccessiveHalving,
pruner_options: %{
min_resource: 1,
reduction_factor: 3,
min_early_stopping_rate: 0
}
}
```
## Future Work
- Hyperband wrapper over SHA
- Patience-based pruning
- Performance curve extrapolation