决策树分类预测python代码？

以下是使用Python中的scikit-learn库实现决策树分类预测的示例代码：

# 导入需要的库
from sklearn.tree import DecisionTreeClassifier
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

# 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=0.2, random_state=42)

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

# 训练模型
clf.fit(X_train, y_train)

# 预测测试集结果
y_pred = clf.predict(X_test)

# 计算准确率
score = clf.score(X_test, y_test)
print("准确率：", score)

代码中首先导入需要的库，然后加载iris数据集，并将数据集划分为训练集和测试集。接着，创建决策树分类器，并使用训练数据对其进行训练。最后，使用测试数据进行预测，并计算模型的准确率。需要注意的是，在实际应用中，还需要对模型进行调参以优化模型性能。

相关问题

简单数据挖掘ID3决策树分类预测python代码

以下是一个简单的基于ID3算法的决策树分类预测的Python代码：

import pandas as pd
import numpy as np
import math

# 定义计算熵的函数
def calc_entropy(data):
    label_col = data.iloc[:, -1]
    _, counts = np.unique(label_col, return_counts=True)
    probs = counts / len(label_col)
    entropy = sum(probs * -np.log2(probs))
    return entropy

# 定义计算信息增益的函数
def calc_info_gain(data, feature):
    entropy_before_split = calc_entropy(data)
    vals, counts = np.unique(data[feature], return_counts=True)
    probs = counts / sum(counts)
    entropy_after_split = 0
    for i in range(len(vals)):
        sub_data = data[data[feature] == vals[i]]
        entropy_after_split += probs[i] * calc_entropy(sub_data)
    info_gain = entropy_before_split - entropy_after_split
    return info_gain

# 定义获取最佳切分特征的函数
def get_best_split_feature(data):
    features = data.columns[:-1]
    best_feature = None
    best_info_gain = -1
    for feature in features:
        info_gain = calc_info_gain(data, feature)
        if info_gain > best_info_gain:
            best_info_gain = info_gain
            best_feature = feature
    return best_feature

# 定义决策树训练函数
def train_decision_tree(data):
    # 终止条件1：如果数据集中所有样本都属于同一类别，直接返回该类别
    if len(np.unique(data.iloc[:, -1])) == 1:
        return np.unique(data.iloc[:, -1])[0]
    # 终止条件2：如果数据集中没有特征可供切分，直接返回样本数最多的类别
    if len(data.columns) == 1:
        return np.bincount(data.iloc[:, -1]).argmax()
    # 选择最佳切分特征
    best_feature = get_best_split_feature(data)
    # 构建决策树
    decision_tree = {best_feature: {}}
    vals, counts = np.unique(data[best_feature], return_counts=True)
    for i in range(len(vals)):
        # 去掉最佳切分特征，递归构建子树
        sub_data = data[data[best_feature] == vals[i]].drop(best_feature, axis=1)
        decision_tree[best_feature][vals[i]] = train_decision_tree(sub_data)
    return decision_tree

# 定义决策树预测函数
def predict(data, tree):
    for feature, subtree in tree.items():
        val = data[feature]
        if val not in subtree:
            return np.bincount(data.iloc[:, -1]).argmax()
        subtree = subtree[val]
        if isinstance(subtree, dict):
            return predict(data.drop(feature, axis=1), subtree)
        else:
            return subtree

# 加载数据集
data = pd.read_csv('data.csv')

# 训练决策树
decision_tree = train_decision_tree(data)

# 预测新样本
new_data = pd.DataFrame({'feature1': [1, 1, 0, 0], 'feature2': [1, 0, 1, 0]})
for i in range(len(new_data)):
    prediction = predict(new_data.iloc[i], decision_tree)
    print('Sample', i+1, 'predicted class:', prediction)

注：此代码仅作为学习参考，实际应用中需要考虑更多的细节和优化措施。

鸢尾花决策树分类的Python代码

在Python中，我们可以使用Scikit-learn库来构建鸢尾花数据集的决策树分类模型。这是一个简单的示例，我们将展示如何使用`Iris`数据集并创建一个决策树分类器：

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

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

# 数据预处理：分割训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

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

# 训练模型
clf.fit(X_train, y_train)

# 预测
y_pred = clf.predict(X_test)

# 计算准确率
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy: {accuracy * 100:.2f}%")

# 演示决策树结构
import graphviz
dot_data = tree.export_graphviz(clf, out_file=None, feature_names=iris.feature_names, class_names=iris.target_names, filled=True, rounded=True)
graphviz.Source(dot_data).view()

在这个例子中，我们首先导入所需的库，然后加载鸢尾花数据。接着将数据分为训练集和测试集，创建一个`DecisionTreeClassifier`实例，训练它，并预测测试集的结果。最后计算预测的准确率，并可视化工作流程。