决策树分类与回归模型的实现和可视化

Last update: Oct 22, 2022

Overview

DecisionTree

决策树分类与回归模型，以及可视化

DecisionTree
- ID3
- C4.5
- CART
  - 分类
  - 回归
- 决策树绘制
  - 分类树
  - 回归树
- 调参
- 剪枝

ID3

ID3决策树是最朴素的决策树分类器：

无剪枝
只支持离散属性
采用信息增益准则

在data.py中，我们记录了一个小的西瓜数据集，用于离散属性的二分类任务。我们可以像下面这样训练一个ID3决策树分类器：

from ID3 import ID3Classifier
from data import load_watermelon2
import numpy as np

X, y = load_watermelon2(return_X_y=True) # 函数参数仿照sklearn.datasets
model = ID3Classifier()
model.fit(X, y)
pred = model.predict(X)
print(np.mean(pred == y))

输出1.0，说明我们生成的决策树是正确的。

C4.5

C4.5决策树分类器对ID3进行了改进：

用信息增益率的启发式方法来选择划分特征；
能够处理离散型和连续型的属性类型，即将连续型的属性进行离散化处理；
剪枝；
能够处理具有缺失属性值的训练数据；

我们实现了前两点，以及第三点中的预剪枝功能（超参数）

在data.py中还有一个连续离散特征混合的西瓜数据集，我们用它来测试C4.5决策树的效果：

from C4_5 import C4_5Classifier
from data import load_watermelon3
import numpy as np

X, y = load_watermelon3(return_X_y=True) # 函数参数仿照sklearn.datasets
model = C4_5Classifier()
model.fit(X, y)
pred = model.predict(X)
print(np.mean(pred == y))

输出1.0，说明我们生成的决策树正确.

CART

分类

CART(Classification and Regression Tree)是C4.5决策树的扩展，支持分类和回归。CART分类树算法使用基尼系数选择特征，此外对于离散特征，CART决策树在每个节点二分划分，缓解了过拟合。

这里我们用sklearn中的鸢尾花数据集测试：

from CART import CARTClassifier
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

X, y = load_iris(return_X_y=True)
train_X, test_X, train_y, test_y = train_test_split(X, y, train_size=0.7)
model = CARTClassifier()
model.fit(train_X, train_y)
pred = model.predict(test_X)
print(accuracy_score(test_y, pred))

准确率95.55%。

回归

CARTRegressor类实现了决策树回归，以sklearn的波士顿数据集为例：

from CART import CARTRegressor
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error

X, y = load_boston(return_X_y=True)
train_X, test_X, train_y, test_y = train_test_split(X, y, train_size=0.7)
model = CARTRegressor()
model.fit(train_X, train_y)
pred = model.predict(test_X)
print(mean_squared_error(test_y, pred))

输出26.352171052631576，sklearn决策树回归的Baseline是22.46，性能近似，说明我们的实现正确。

决策树绘制

分类树

利用python3的graphviz第三方库和Graphviz(需要安装)，我们可以将决策树可视化：

from plot import tree_plot
from CART import CARTClassifier
from sklearn.datasets import load_iris

X, y = load_iris(return_X_y=True)
model = CARTClassifier()
model.fit(X, y)
tree_plot(model)

运行，文件夹中生成tree.png：

如果提供了特征的名词和标签的名称，决策树会更明显：

from plot import tree_plot
from CART import CARTClassifier
from sklearn.datasets import load_iris

iris = load_iris()
model = CARTClassifier()
model.fit(iris.data, iris.target)
tree_plot(model,
          filename="tree2",
          feature_names=iris.feature_names,
          target_names=iris.target_names)

绘制西瓜数据集2对应的ID3决策树：

from plot import tree_plot
from ID3 import ID3Classifier
from data import load_watermelon2

watermelon = load_watermelon2()
model = ID3Classifier()
model.fit(watermelon.data, watermelon.target)
tree_plot(
    model,
    filename="tree",
    font="SimHei",
    feature_names=watermelon.feature_names,
    target_names=watermelon.target_names,
)

这里要自定义字体，否则无法显示中文：

回归树

用同样的方法，我们可以进行回归树的绘制：

from plot import tree_plot
from ID3 import ID3Classifier
from sklearn.datasets import load_boston

boston = load_boston()
model = ID3Classifier(max_depth=5)
model.fit(boston.data, boston.target)
tree_plot(
    model,
    feature_names=boston.feature_names,
)

由于生成的回归树很大，我们限制最大深度再绘制：

调参

CART和C4.5都是有超参数的，我们让它们作为sklearn.base.BaseEstimator的派生类，借助sklearn的GridSearchCV，就可以实现调参：

from plot import tree_plot
from CART import CARTClassifier
from sklearn.datasets import load_wine
from sklearn.model_selection import train_test_split, GridSearchCV

wine = load_wine()
train_X, test_X, train_y, test_y = train_test_split(
    wine.data,
    wine.target,
    train_size=0.7,
)
model = CARTClassifier()
grid_param = {
    'max_depth': [2, 4, 6, 8, 10],
    'min_samples_leaf': [1, 3, 5, 7],
}

search = GridSearchCV(model, grid_param, n_jobs=4, verbose=5)
search.fit(train_X, train_y)
best_model = search.best_estimator_
print(search.best_params_, search.best_estimator_.score(test_X, test_y))
tree_plot(
    best_model,
    feature_names=wine.feature_names,
    target_names=wine.target_names,
)

输出最优参数和最优模型在测试集上的表现：

{'max_depth': 4, 'min_samples_leaf': 3} 0.8518518518518519

绘制对应的决策树：

剪枝

在ID3和CART回归中加入了REP剪枝，C4.5则支持了PEP剪枝。

对IRIS数据集训练后的决策树进行PEP剪枝：

iris = load_iris()
model = C4_5Classifier()
X, y = iris.data, iris.target
train_X, test_X, train_y, test_y = train_test_split(X, y, train_size=0.7)
model.fit(train_X, train_y)
print(model.score(test_X, test_y))
tree_plot(model,
          filename="src/pre_prune",
          feature_names=iris.feature_names,
          target_names=iris.target_names)
model.pep_pruning()
print(model.score(test_X, test_y))
tree_plot(model,
          filename="src/post_prune",
          feature_names=iris.feature_names,
          target_names=iris.target_names,
)

剪枝前后的准确率分别为97.78%，100%，即泛化性能的提升：

决策树分类与回归模型的实现和可视化

Related tags

Overview

DecisionTree

ID3

C4.5

CART

分类

回归

决策树绘制

分类树

回归树

调参

剪枝

Owner

Welt Xing

flexible time-series processing & feature extraction

A Python implementation of GRAIL, a generic framework to learn compact time series representations.

Implemented four supervised learning Machine Learning algorithms

Provide an input CSV and a target field to predict, generate a model + code to run it.

Retrieve annotated intron sequences and classify them as minor (U12-type) or major (U2-type)

MachineLearningStocks is designed to be an intuitive and highly extensible template project applying machine learning to making stock predictions.

Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.

A Python package for time series classification

Evidently helps analyze machine learning models during validation or production monitoring

A Streamlit demo to interactively visualize Uber pickups in New York City

Napari sklearn decomposition

Combines MLflow with a database (PostgreSQL) and a reverse proxy (NGINX) into a multi-container Docker application

Implementation of different ML Algorithms from scratch, written in Python 3.x

Machine Learning Algorithms ( Desion Tree, XG Boost, Random Forest )

2D fluid simulation implementation of Jos Stam paper on real-time fuild dynamics, including some suggested extensions.

Educational python for Neural Networks, written in pure Python/NumPy.

Turns your machine learning code into microservices with web API, interactive GUI, and more.

TensorFlow Decision Forests (TF-DF) is a collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models.

A library of sklearn compatible categorical variable encoders

BASTA: The BAyesian STellar Algorithm