决策树分类与回归模型的实现和可视化

Overview

DecisionTree

决策树分类与回归模型,以及可视化

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

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)

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

iris_tree

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)

iris_tree2

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:

watermelon

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:

regression

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:

wine

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:

prepre

Owner
Welt Xing
Undergraduate in AI school, Nanjing University. Main interest(for now): Machine learning and deep learning.
Welt Xing
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

Website | Documentation | Tutorials | Installation | Release Notes CatBoost is a machine learning method based on gradient boosting over decision tree

CatBoost 6.9k Jan 05, 2023
Implementation of linesearch Optimization Algorithms in Python

Nonlinear Optimization Algorithms During my time as Scientific Assistant at the Karlsruhe Institute of Technology (Germany) I implemented various Opti

Paul 3 Dec 06, 2022
50% faster, 50% less RAM Machine Learning. Numba rewritten Sklearn. SVD, NNMF, PCA, LinearReg, RidgeReg, Randomized, Truncated SVD/PCA, CSR Matrices all 50+% faster

[Due to the time taken @ uni, work + hell breaking loose in my life, since things have calmed down a bit, will continue commiting!!!] [By the way, I'm

Daniel Han-Chen 1.4k Jan 01, 2023
Using Logistic Regression and classifiers of the dataset to produce an accurate recall, f-1 and precision score

Using Logistic Regression and classifiers of the dataset to produce an accurate recall, f-1 and precision score

Thines Kumar 1 Jan 31, 2022
Estudos e projetos feitos com PySpark.

PySpark (Spark com Python) PySpark é uma biblioteca Spark escrita em Python, e seu objetivo é permitir a análise interativa dos dados em um ambiente d

Karinne Cristina 54 Nov 06, 2022
A simple example of ML classification, cross validation, and visualization of feature importances

Simple-Classifier This is a basic example of how to use several different libraries for classification and ensembling, mostly with sklearn. Example as

Rob 2 Aug 25, 2022
LinearRegression2 Tvads and CarSales

LinearRegression2_Tvads_and_CarSales This project infers the insight that how the TV ads for cars and car Sales are being linked with each other. It i

Ashish Kumar Yadav 1 Dec 29, 2021
A quick reference guide to the most commonly used patterns and functions in PySpark SQL

Using PySpark we can process data from Hadoop HDFS, AWS S3, and many file systems. PySpark also is used to process real-time data using Streaming and

Sundar Ramamurthy 53 Dec 21, 2022
Used Logistic Regression, Random Forest, and XGBoost to predict the outcome of Search & Destroy games from the Call of Duty World League for the 2018 and 2019 seasons.

Call of Duty World League: Search & Destroy Outcome Predictions Growing up as an avid Call of Duty player, I was always curious about what factors led

Brett Vogelsang 2 Jan 18, 2022
Real-time domain adaptation for semantic segmentation

Advanced-Machine-Learning This repository contains the code for the project Real

Andrea Cavallo 1 Jan 30, 2022
Tribuo - A Java machine learning library

Tribuo - A Java prediction library (v4.1) Tribuo is a machine learning library in Java that provides multi-class classification, regression, clusterin

Oracle 1.1k Dec 28, 2022
An open source framework that provides a simple, universal API for building distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyperparameter tuning library.

Ray provides a simple, universal API for building distributed applications. Ray is packaged with the following libraries for accelerating machine lear

23.3k Dec 31, 2022
ML Optimizers from scratch using JAX

Toy implementations of some popular ML optimizers using Python/JAX

Shreyansh Singh 38 Jul 29, 2022
Winning solution for the Galaxy Challenge on Kaggle

Winning solution for the Galaxy Challenge on Kaggle

Sander Dieleman 483 Jan 02, 2023
Extreme Learning Machine implementation in Python

Python-ELM v0.3 --- ARCHIVED March 2021 --- This is an implementation of the Extreme Learning Machine [1][2] in Python, based on scikit-learn. From

David C. Lambert 511 Dec 20, 2022
MCML is a toolkit for semi-supervised dimensionality reduction and quantitative analysis of Multi-Class, Multi-Label data

MCML is a toolkit for semi-supervised dimensionality reduction and quantitative analysis of Multi-Class, Multi-Label data. We demonstrate its use

Pachter Lab 26 Nov 29, 2022
An open-source library of algorithms to analyse time series in GPU and CPU.

An open-source library of algorithms to analyse time series in GPU and CPU.

Shapelets 216 Dec 30, 2022
Predict the income for each percentile of the population (Python) - FRENCH

05.income-prediction Predict the income for each percentile of the population (Python) - FRENCH Effectuez une prédiction de revenus Prérequis Pour ce

1 Feb 13, 2022
Automated Machine Learning with scikit-learn

auto-sklearn auto-sklearn is an automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator. Find the documentation here

AutoML-Freiburg-Hannover 6.7k Jan 07, 2023