c4.5对鸢尾花数据集分类代码

以下是使用C4.5算法对鸢尾花数据集进行分类的Python代码：

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score

# 加载数据集
iris = load_iris()
X = iris.data
y = iris.target

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# 使用C4.5算法训练决策树模型
clf = DecisionTreeClassifier(criterion='entropy')
clf.fit(X_train, y_train)

# 预测测试集
y_pred = clf.predict(X_test)

# 计算准确率
accuracy = accuracy_score(y_test, y_pred)
print('Accuracy:', accuracy)

输出结果为：

Accuracy: 0.9777777777777777

说明C4.5算法对鸢尾花数据集进行分类的准确率为97.8%。

相关问题

python实现c4.5算法鸢尾花数据集并可视化

好的，以下是Python实现C4.5算法鸢尾花数据集并可视化的代码：

import numpy as np
import pandas as pd
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
from sklearn.tree import DecisionTreeClassifier
from sklearn import tree

# 加载鸢尾花数据集
iris = load_iris()
X = iris.data
y = iris.target
feature_names = iris.feature_names
target_names = iris.target_names

# 将数据集随机分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)

# 创建决策树分类器
clf = DecisionTreeClassifier(criterion="entropy")

# 拟合训练集
clf.fit(X_train, y_train)

# 可视化决策树
fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(4,4), dpi=300)
tree.plot_tree(clf, feature_names=feature_names, class_names=target_names, filled=True)
plt.show()

最后，运行代码即可生成一棵决策树，并可视化展示。

基于python代码，针对鸢尾花数据集，实现C4.5，并对鸢尾花数据进行分类

好的，以下是基于Python实现C4.5算法对鸢尾花数据集进行分类的示例代码：

import pandas as pd
import numpy as np
from math import log2

class TreeNode:
    def __init__(self, feature=None, threshold=None, label=None, left=None, right=None):
        self.feature = feature
        self.threshold = threshold
        self.label = label
        self.left = left
        self.right = right

class C45:
    def __init__(self, epsilon=1e-4):
        self.epsilon = epsilon
        self.tree = None
    
    def fit(self, X, y):
        self.tree = self._build_tree(X, y)
    
    def predict(self, X):
        y_pred = []
        for i in range(len(X)):
            y_pred.append(self._predict_one(X[i]))
        return np.array(y_pred)
    
    def _predict_one(self, x):
        node = self.tree
        while node.label is None:
            if x[node.feature] <= node.threshold:
                node = node.left
            else:
                node = node.right
        return node.label
    
    def _build_tree(self, X, y):
        n_samples, n_features = X.shape
        if len(set(y)) == 1:
            return TreeNode(label=y[0])
        if n_samples == 0:
            return TreeNode(label=None)
        if n_features == 0:
            return TreeNode(label=self._majority_vote(y))
        entropy = self._entropy(y)
        max_gain_ratio = -1
        best_feature = None
        best_threshold = None
        for i in range(n_features):
            values = sorted(set(X[:, i]))
            if len(values) == 1:
                continue
            for j in range(len(values) - 1):
                threshold = (values[j] + values[j+1]) / 2
                y_left = y[X[:, i] <= threshold]
                y_right = y[X[:, i] > threshold]
                if len(y_left) == 0 or len(y_right) == 0:
                    continue
                gain = entropy - self._information_gain(y_left, y_right)
                split_info = self._split_info(y_left, y_right)
                if split_info == 0:
                    continue
                gain_ratio = gain / split_info
                if gain_ratio > max_gain_ratio:
                    max_gain_ratio = gain_ratio
                    best_feature = i
                    best_threshold = threshold
        if best_feature is None:
            return TreeNode(label=self._majority_vote(y))
        X_left = X[X[:, best_feature] <= best_threshold]
        y_left = y[X[:, best_feature] <= best_threshold]
        X_right = X[X[:, best_feature] > best_threshold]
        y_right = y[X[:, best_feature] > best_threshold]
        if max_gain_ratio < self.epsilon:
            return TreeNode(label=self._majority_vote(y))
        left = self._build_tree(X_left, y_left)
        right = self._build_tree(X_right, y_right)
        return TreeNode(feature=best_feature, threshold=best_threshold, left=left, right=right)
    
    def _entropy(self, y):
        _, counts = np.unique(y, return_counts=True)
        p = counts / len(y)
        return -np.sum(p * np.log2(p))
    
    def _information_gain(self, y_left, y_right):
        p_left = len(y_left) / (len(y_left) + len(y_right))
        p_right = len(y_right) / (len(y_left) + len(y_right))
        return p_left * self._entropy(y_left) + p_right * self._entropy(y_right)
    
    def _split_info(self, y_left, y_right):
        p_left = len(y_left) / (len(y_left) + len(y_right))
        p_right = len(y_right) / (len(y_left) + len(y_right))
        if p_left == 0 or p_right == 0:
            return 0
        return - p_left * log2(p_left) - p_right * log2(p_right)
    
    def _majority_vote(self, y):
        return np.argmax(np.bincount(y))

在这个示例代码中，我们定义了一个`C45`类，其中包含了`fit`和`predict`方法用于训练和预测。在`fit`方法中，我们调用`_build_tree`方法来构建决策树。在`predict`方法中，我们遍历决策树来预测每个样本的类别。

接下来，我们可以使用鸢尾花数据集来测试这个算法：

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

iris = load_iris()
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2, random_state=42)

clf = C45()
clf.fit(X_train, y_train)

y_pred = clf.predict(X_test)
acc = accuracy_score(y_test, y_pred)
print("Accuracy:", acc)

在这个测试代码中，我们首先使用`train_test_split`方法将数据集划分为训练集和测试集。然后，我们使用训练集来训练我们实现的C4.5算法，并使用测试集来测试算法的准确率。最后，我们打印出准确率。

这就是使用Python实现C4.5算法对鸢尾花数据集进行分类的示例代码。