如何从 SkLearn 管道中绘制决策树？

Question

所以我正在研究 SkLearn 管道中的决策树。该模型工作正常。但是，我无法绘制决策树。我不确定通过调用 .plot 方法使用哪个对象。

这是我创建决策树模型的代码：

from sklearn.compose import ColumnTransformer
from sklearn.ensemble import RandomForestClassifier
from sklearn import tree
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline, make_pipeline
from sklearn.preprocessing import (
    OneHotEncoder, PowerTransformer, StandardScaler
  )

# Build categorical preprocessor
categorical_cols = X.select_dtypes(include="object").columns.to_list()
categorical_pipe = make_pipeline(
    OneHotEncoder(sparse=False, handle_unknown="ignore")
  )

# Build numeric processor
to_log = ["SA13_peopleHH"]
to_scale = ["SA11_age"]
numeric_pipe_1 = make_pipeline(PowerTransformer())
numeric_pipe_2 = make_pipeline(StandardScaler())

# Full processor
full = ColumnTransformer(
    transformers=[
        ("categorical", categorical_pipe, categorical_cols),
        ("power_transform", numeric_pipe_1, to_log),
        ("standardization", numeric_pipe_2, to_scale),
    ]
)

# Final pipeline combined with DecisionTree
pipeline = Pipeline(
    steps=[
        ("preprocess", full),
        (
            "base",
            DecisionTreeClassifier(),
        ),
    ]
)
# Fit
_ = pipeline.fit(X_train, y_train)

这就是我调用 .plot 函数的方式：

tree.plot_tree(pipeline)

Answer 1

来自：

我认为，tree.plot_tree(pipeline['base']) 可以

如何从 SkLearn 管道中绘制决策树？

How to plot the DecisionTree out of a SkLearn Pipeline?

python

decision-tree

scikit-learn