下载或者导入鸢尾花数据集，任选两个类别并使用logistics回归实现分类模型，并绘制出该模型的分类结果，并保存模型。

目录

代码：

运行结果：

import numpy as np

import matplotlib.pyplot as plt

from sklearn import datasets

from sklearn.model_selection import train_test_split

from sklearn.preprocessing import StandardScaler

from sklearn.linear_model import LogisticRegression

from sklearn.metrics import accuracy_score

iris = datasets.load_iris()

X = iris.data

y = iris.target

X_binary = X[y != 2]

y_binary = y[y != 2]

X_train, X_test, y_train, y_test = train_test_split(X_binary, y_binary, test_size=0.2, random_state=42)

scaler = StandardScaler()

X_train_scaled = scaler.fit_transform(X_train)

X_test_scaled = scaler.transform(X_test)

model = LogisticRegression()

model.fit(X_train_scaled, y_train)

y_pred = model.predict(X_test_scaled)

accuracy = accuracy_score(y_test, y_pred)

print(f'模型准确度: {accuracy:.2f}')

plt.figure(figsize=(8, 6))

plt.sca

python