决策树回归代码，运行时仅显示'random_state = 0'而不显示其他任何内容

Question

我取了一个房价数据集。我运行了以下代码:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.tree import DecisionTreeRegressor
from sklearn.model_selection import train_test_split
data = pd.read_csv(r'C:UsersindurDesktopPython projectssupervised-regression-projectdatasetshouseprice.csv')
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.33, random_state=0)
dtr = DecisionTreeRegressor(random_state = 0)
dtr.fit(x_train, y_train)

这是我得到的输出:

DecisionTreeRegressor(random_state=0)

这是我想要得到的输出:

DecisionTreeRegressor(criterion = 'mse', max_depth=None, max_features=None, max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, min_samples_leaf=1,min_samples_split=2, min_weight_fraction_leaf=0.0,presort=False, random_state=0, splitter='best')

如何得到后一个输出?

Answer 1

从scikit-learn v0.23开始，显示配置必须像这样设置:

from sklearn import set_config
set_config(print_changed_only=False)