sampling.effects.plot_all_partial_dependence

sampling.effects.plot_all_partial_dependence(
    df,
    df_target,
    model='GradientBoostingRegressor',
    nrows=5,
    ncols=6,
    figsize=(20, 15),
    title='',
)

Generates Partial Dependence Plots (PDPs) for every feature in a DataFrame against a target variable, arranged in a grid.

Parameters

Name	Type	Description	Default
df	pd.DataFrame	DataFrame containing the features.	required
df_target	pd.Series	Series containing the target variable.	required
model	str	Name of the model class to use (e.g., “GradientBoostingRegressor”). Defaults to “GradientBoostingRegressor”.	'GradientBoostingRegressor'
nrows	int	Number of rows in the grid of subplots. Defaults to 5.	5
ncols	int	Number of columns in the grid of subplots. Defaults to 6.	6
figsize	tuple	Figure size (width, height) in inches. Defaults to (20, 15).	(20, 15)
title	str	Title for the subplots. Defaults to ““.	''

Returns

Name	Type	Description
	None	None

Examples

>>> form spotpython.utils.effects import plot_all_partial_dependence
>>> from sklearn.datasets import load_boston
>>> import pandas as pd
>>> data = load_boston()
>>> df = pd.DataFrame(data.data, columns=data.feature_names)
>>> df_target = pd.Series(data.target, name="target")
>>> plot_all_partial_dependence(df, df_target, model="GradientBoostingRegressor", nrows=5, ncols=6, figsize=(20, 15))