ModelDiagnostic¶
-
class
protopipe.mva.
ModelDiagnostic
(model, feature_name_list, target_name)[source]¶ Bases:
object
Base class for model diagnostics.
- Parameters
- model: `~sklearn.base.BaseEstimator`
Best model
- feature_name_list: list
List of the features used to buil the model
- target_name: str
Name of the target (e.g. score, gamaness, energy, etc.)
Methods Summary
plot_feature_importance
(ax, **kwargs)Plot importance of features
plot_features
(data_list[, nbin, …])Plot model features for different data set (e.g.
Methods Documentation
-
plot_feature_importance
(ax, **kwargs)[source]¶ Plot importance of features
- Parameters
- ax: `~matplotlib.axes.Axes`
Axis
-
plot_features
(data_list, nbin=30, hist_kwargs_list={}, error_kw_list={}, ncols=2)[source]¶ Plot model features for different data set (e.g. training and test samples).
- Parameters
- data_list: list
List of data
- nbin: int
Number of bin
- hist_kwargs_list: dict
Dictionary with histogram options
- error_kw_list: dict
Dictionary with error bar options
- ncols: int
Number of columns