SHAP values for skforecast models¶
SHAP (SHapley Additive exPlanations) values are a popular method for explaining machine learning models, as they help to understand how variables and values influence predictions visually and quantitatively.
It is possible to generate SHAP-values explanations from Skforecast models with just two essential elements:
The internal regressor of the forecaster.
The training matrices created from the time series and used to fit the forecaster.
By leveraging these two components, users can create insightful and interpretable explanations for their skforecast models. These explanations can be used to verify the reliability of the model, identify the most significant factors that contribute to model predictions, and gain a deeper understanding of the underlying relationship between the input variables and the target variable.
Note
See also: Feature importances
Libraries¶
# Libraries
# ==============================================================================
import pandas as pd
import shap
shap.initjs()
from sklearn.ensemble import HistGradientBoostingRegressor
from skforecast.ForecasterAutoreg import ForecasterAutoreg
Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)
