【集成学习】随机森林实现鸢尾花分类

最新推荐文章于 2025-03-12 15:36:54 发布

原创最新推荐文章于 2025-03-12 15:36:54 发布 · 5.9k 阅读

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本文通过使用随机森林分类器对鸢尾花数据集进行预测，展示了如何应用随机森林进行分类任务，包括数据预处理、模型训练、预测及评估，同时提供了可视化结果。

Yolo-v8.3

Yolo

YOLO（You Only Look Once）是一种流行的物体检测和图像分割模型，由华盛顿大学的Joseph Redmon 和Ali Farhadi 开发。 YOLO 于2015 年推出，因其高速和高精度而广受欢迎

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.datasets import load_iris
from sklearn.preprocessing import StandardScaler
import matplotlib as mpl
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
from sklearn.metrics import accuracy_score

mpl.rcParams['font.sans-serif'] = ['SimHei']
mpl.rcParams['axes.unicode_minus'] = False
RF = RandomForestClassifier(n_estimators=100, n_jobs=4, oob_score=True)
iris = load_iris()
X1 = iris.data
y = iris.target
X_train, X_test, y_train, y_test = train_test_split(X1, y, test_size=0.25, random_state=0)
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
# classifier = RandomForestClassifier(n_estimators=10, criterion='entropy', random_state=0)
RF.fit(X_train, y_train)
y_pred = RF.predict(X_test)
print("随机森林准确率:", accuracy_score(y_test, y_pred))
print("其他指标：\n", classification_report(y_test, y_pred, target_names=['0', '1', '2']))
# 混淆矩阵在二分类和多分类中的使用 https://blog.youkuaiyun.com/Orange_Spotty_Cat/article/details/80520839


# 画图
x = iris.data[:, :2]
RF.fit(x, y)
N = 50
cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])
cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])
# for weight in ['uniform', 'distance']:
x_min, x_max = x[:, 0].min() - 1, x[:, 0].max() + 1
y_min, y_max = x[:, 1].min() - 1, x[:, 1].max() + 1
xx, yy = np.meshgrid(np.linspace(x_min, x_max, N), np.linspace(y_min, y_max, N))
z_show = np.stack((xx.flat, yy.flat), axis=1)  # 测试点
# z = RF.predict(np.c_[xx.ravel(), yy.ravel()])
z = RF.predict(z_show)
plt.figure()
plt.pcolormesh(xx, yy, z.reshape(xx.shape), shading='auto', cmap=cmap_light)
plt.scatter(x[:, 0], x[:, 1], c=y, cmap=cmap_bold, edgecolors='k', s=20)
plt.xlim(xx.min(), xx.max())
plt.title('RandomForestClassifier')
plt.show()
# print('RandomForestClassifier:', RF.score(x, y))

运行效果：