下载或者导入鸢尾花数据集，任选两个类别并使用logistics回归正则化实现分类模型，比较不同正则化结果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

from sklearn.model_selection import GridSearchCV

import joblib

iris = datasets.load_iris()

X = iris.data

y = iris.target

X = X[y != 2]

y = y[y != 2]

X_train, X_test, y_train, y_test = train_test_split(X, y, 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()

param_grid = {'C': [0.001, 0.01, 0.1, 1, 10, 100]}

grid_search = GridSearchCV(model, param_grid, cv=5)

grid_search.fit

python