# `EpsilonGreedy`

### *class* capymoa.automl.EpsilonGreedy[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/automl/_bandit_classifier.py#L16)

Bases: [`object`](https://docs.python.org/3/builtins/functions.html#object)

Epsilon-Greedy bandit policy for model selection.

This policy selects the best model with probability `1 - epsilon` and explores
other models with probability `epsilon`. During the burn-in period, it always
explores to gather initial information about all models.

```pycon
>>> from capymoa.automl import EpsilonGreedy
>>> policy = EpsilonGreedy(epsilon=0.1, burn_in=50)
>>> policy.epsilon
0.1
```

#### SEE ALSO
[`BanditClassifier`](capymoa.automl.BanditClassifier.md#capymoa.automl.BanditClassifier)

#### \_\_init_\_(epsilon: [float](https://docs.python.org/3/builtins/functions.html#float) = 0.1, burn_in: [int](https://docs.python.org/3/builtins/functions.html#int) = 100)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/automl/_bandit_classifier.py#L33)

Construct a new Epsilon-Greedy policy.

* **Parameters:**
  * **epsilon** – Probability of exploring a random model (default: `0.1`).
  * **burn_in** – Number of initial rounds dedicated to exploration (default: `100`).

#### get_arm_stats()[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/automl/_bandit_classifier.py#L94)

Get statistics about each arm’s performance.

#### get_best_arm_idx(available_arms)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/automl/_bandit_classifier.py#L64)

#### initialize(n_arms)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/automl/_bandit_classifier.py#L57)

Initialize the policy with a given number of arms.

#### pull(available_arms)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/automl/_bandit_classifier.py#L71)

Select which arms to pull based on the epsilon-greedy policy.

#### update(arm, reward)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/automl/_bandit_classifier.py#L88)

Update the policy with the observed reward for the pulled arm.

#### arm_counts

Number of times each arm has been pulled.

#### arm_rewards

Cumulative reward values for each model (arm).

#### burn_in

Number of initial rounds where all models are explored to collect initial statistics.

#### epsilon

Probability of exploring a random model.

#### n_arms

Number of available models (arms).

#### total_pulls

Total number of model selections performed.