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from sklearn.datasets import load_bostonboston = load_boston()boston_data = boston['data']boston_target = boston['target']boston_names = boston['feature_names']print('boston数据集数据的形状为：',boston_data.shape)print('boston数据集标签的形状为：',boston_target.shape).

from sklearn.datasets import load_irisiris = load_iris()iris_data = iris['data'] ##提取数据集中的特征iris_target = iris['target'] ## 提取数据集中的标签iris_names = iris['feature_names'] ### 提取特征名print('iris数据集的特征名为：\n',iris_names)#sepal：花萼，petal：花瓣iris特征集的特征名为.

# 使用LogisticRegression类构建Logistic回归模型from sklearn.linear_model import LogisticRegressionlr_model = LogisticRegression(solver='saga')# 训练Logistic回归模型lr_model.fit(x_trainStd, y_train)#print('训练出来的LogisticRegression模型为：\n', lr_model)print('各特征的相关系数为：.

from sklearn.datasets import load_bostonfrom sklearn.model_selection import train_test_split# 导入load_boston数据，波士顿房价数据boston = load_boston()x = boston['data']y = boston['target']names = boston['feature_names']# 将数据划分为训练集和测试集x_train, x_test, y_tra.

import numpy as npfrom scipy import optimizeimport matplotlib.pyplot as pltfrom matplotlib import rcParamsrcParams['font.sans-serif'] = 'SimHei'rcParams['axes.unicode_minus'] = False# 定义原始函数def func(x,p): ''' x：表示函数的未知数x。 p：接收tuple，.

from sklearn.datasets import load_breast_cancerfrom sklearn.model_selection import train_test_split# 导入load_breast_cancer数据cancer = load_breast_cancer()x = cancer['data']y = cancer['target']# 将数据划分为训练集测试集x_train, x_test, y_train, y_test = train_te.

import numpy as npimport matplotlib.pyplot as pltimport matplotlib.font_managerfrom sklearn import svm# Generate train dataX = 0.3 * np.random.randn(100, 2)#np.r_是按列连接两个矩阵，就是把两矩阵上下相加，要求列数相等。#np.c_是按行连接两个矩阵，就是把两矩阵左右相加，要求行数相等。X_train = np.r_[X + 2,.

from sklearn.model_selection import validation_curvefrom sklearn.datasets import load_digitsfrom sklearn.svm import SVCimport matplotlib.pyplot as pltimport numpy as npdigits = load_digits()X = digits.datay = digits.target#建立参数测试集param_range = .

from sklearn.model_selection import learning_curvefrom sklearn.datasets import load_digitsfrom sklearn.svm import SVCimport matplotlib.pyplot as pltimport numpy as npdigits = load_digits()X = digits.datay = digits.targettrain_sizes, train_loss, te.

from sklearn import datasetsiris = datasets.load_iris()from sklearn.model_selection import train_test_splitX_train,X_test,y_train,y_test=train_test_split(iris.data,iris.target,test_size=0.4,random_state=0)from sklearn import svm#建立模型svc = svm.SVC(.