hparam_optim#

caf.brain.ml.hparam_optim(model_choice, data, output_path, target, is_time_series=None, weight=None, classification_prediction=None)[source]#

Hyperparameter optimisation for your selected algorithm.

Algorithm must be part of the Models enum class.

Parameters:

  • model_choice (Models) – List or one algorithm to use as the base of the model. Available algorithms can be seen in _ml_inputs.py or __info__.py.

  • is_time_series (bool | None) – If true then data must be time series. Time series based characteristics are taken into consideration during function execution.

  • data (DataFrame) – Pandas Dataframe of your data. Structured or semi-structured tabular format.

  • output_path (Path | str) – Path to output location.

  • target (str) – Column in your data that is the target variable (Y, dependent variable), what you want to predict.

  • weight (str | None) – Optional string column value to be used as weight.

  • classification_prediction (tuple[int, ...] | None) – List of integers that correspond to the target column. The value(s) to predict in a classification problem.

Return type:

Initialised model with the best combination of hyperparameters.