from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split # 1、获取鸢尾花数据集 iris = load_iris() # 对鸢尾花数据集进行分割 # 训练集的特征值x_train 测试集的特征值x_test 训练集的目标值y_train 测试集的目标值y_test x_train, x_test, y_train, y_test = train_test_split(iris.data, iris.target, random_state=22) print("x_train:\n", x_train.shape) # 随机数种子 x_train1, x_test1, y_train1, y_test1 = train_test_split(iris.data, iris.target, random_state=6) x_train2, x_test2, y_train2, y_test2 = train_test_split(iris.data, iris.target, random_state=6) print("如果随机数种子不一致：\n", x_train == x_train1) print("如果随机数种子一致：\n", x_train1 == x_train2)请根据上述代码写一份详细解析

Python数据集乳腺癌数据集（from sklearn.datasets import load-breast-cancer）

Python数据集乳腺癌数据集（from sklearn.datasets import load_breast_cancer） Python数据集乳腺癌数据集（from sklearn.datasets import load_breast_cancer） Python数据集乳腺癌数据集（from sklearn.datasets ...

20newsgroup数据集-机器学习-标准数据集（all）下载方式 from sklearn.datasets import fetch_20newsgrou

20newsgroup数据集是机器学习中的一个标准数据集。它包含18828个文档，来自于20个不同的新闻组。如果把每个新闻组看作是一个簇，那么很容易测试出我们寻找相关文档的方法是否有效。

Boston-Model-Housing-prices-Multiple-Regression:使用多元回归模型从sklearn.datasets.load_boston预测房价

# 导入所需的库和数据集 from sklearn.ensemble import RandomForestClassifier from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split from sklearn.metrics import classification_report, confusion_matrix # 加载鸢尾花数据集 iris = load_iris() # 数据预处理 # 检查数据集的特征和标签 X = y = # 划分数据集 X_train, X_test, y_train, y_test =

from sklearn.model_selection import train_test_split from sklearn.metrics import classification_report, confusion_matrix # 加载鸢尾花数据集 iris = load_iris() # 数据预处理 # 检查数据集的特征和标签 X...

运用python语言，使用SKLearn中任一数据集（如鸾尾花数据集from sklearn.datasets import load_iris；乳腺癌数据集from sklearn.datasets import load_breast_cancer；波士顿房价数据集from sklearn.datasets import load_boston等等），调用决策树算法进行一次分类或回归任务。

from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score # 加载鸢尾花数据集 iris = load_iris() X, y = iris.data, iris.target 然后，我们将数据集划分为训练集...

from sklearn.ensemble import GradientBoostingClassifier from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split, GridSearchCV from sklearn.metrics import accuracy_score from sklearn.tree import export_graphviz import graphviz from IPython.display import display # 加载iris数据集 iris = load_iris() X = iris.data y = iris.target什么意思

从 sklearn.datasets 中导入了 load_iris 函数，用于加载鸢尾花数据集；从 sklearn.model_selection 中导入了 train_test_split 和 GridSearchCV 函数，用于划分训练集和测试集，并进行网格搜索优化模型...

import numpy as np import matplotlib.pyplot as plt from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split啥意思

具体来说，该程序使用load_iris函数加载鸢尾花数据集，并使用train_test_split函数将数据集随机划分为训练集和测试集，以便进行模型训练和评估。另外，还使用了numpy和matplotlib.pyplot库进行数据处理和可视化。...

import numpy as np import matplotlib.pyplot as plt from sklearn.datasets import load_iris from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split，这是什么意思

具体来说，该程序使用load_iris函数加载鸢尾花数据集，然后使用train_test_split函数将数据集随机划分为训练集和测试集。接着，使用LogisticRegression函数创建一个逻辑回归分类器，并使用训练集进行拟合。最后，...

import numpy as np from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split from sklearn.neighbors import KNeighborsClassifier iris_data = load_iris() # 构造训练数据和测试数据 # 构造KNN模型 # 训练模型 # 评估模型 # 做出预测 X_new = np.array([[1.1,5.9,1.4,2.2]]) #给出X_new预测结果

from sklearn.model_selection import train_test_split from sklearn.neighbors import KNeighborsClassifier iris_data = load_iris() # 构造训练数据和测试数据 X_train, X_test, y_train, y_test = train_...

import sklearn.datasets as datasets # 导入数据库 from sklearn.neighbors import KNeighborsClassifier # 导入KNN分类算法 from sklearn.model_selection import train_test_split # 导入数据集划分器 # 1.捕获鸢尾花数据 iris = datasets.load_iris() # 2.提取样本数据 feature = iris['data'] # 实现

from sklearn.model_selection import train_test_split # 1. 捕获鸢尾花数据 iris = datasets.load_iris() # 2. 提取样本数据，即特征和标签 X = iris.data y = iris.target # 3. 数据集划分 X_train, X_test, y...

from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeClassifier # 加载鸢尾花数据集 from sklearn.datasets import load_iris iris = load_iris() # 将数据集分成训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.3, random_state=42) # 输出训练集和测试集的大小 print("训练集大小：", X_train.shape) print("测试集大小：", X_test.shape) # 训练模型 clf = DecisionTreeClassifier() clf.fit(X_train, y_train) # 预测测试集 y_pred = clf.predict(X_test) print(y_pred)这段代码预测了什么

这是Python中的一些库...其中，load_iris是一个数据集，包含了150个鸢尾花的样本数据，每个样本有4个特征，分别是花萼长度、花萼宽度、花瓣长度和花瓣宽度。train_test_split是用于将数据集分为训练集和测试集的函数。...

请分析这段代码:from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split iris = load_iris() X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2, random_state=42)

这段代码的意义是使用scikit-learn库中的load_iris()函数加载鸢尾花数据集，将数据集划分为训练集和测试集，并将相应的特征数据和标签数据储存在X_train, X_test, y_train, y_test这四个变量中。其中test_size是用来...

from sklearn.datasets import load_irisfrom sklearn.tree import DecisionTreeClassifierfrom sklearn.model_selection import train_test_splitfrom sklearn.metrics import accuracy_score# 加载鸢尾花数据集iris = load_iris()X, y = iris.data, 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)# 构建决策树分类器clf = DecisionTreeClassifier()# 训练模型clf.fit(X_train, y_train)# 预测测试集y_pred = clf.predict(X_test)# 计算准确率acc = accuracy_score(y_test, y_pred)print("Accuracy:", acc)怎么显示每步代码的运行结果

from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score # 加载鸢尾花数据集 iris = load_iris() X, y = iris.data, iris.target # 划分训练集和测试集 X_train, X_...

from sklearn import datasets from sklearn.model_selection import train_test_split from sklearn.neighbors import KNeighborsClassifier from sklearn.metrics import accuracy_score iris = datasets.load_iris() iris_X = iris.data iris_y = iris.target X_train, X_test, y_train, y_test = train_test_split(iris_X, iris_y, test_size=0.3) knn = KNeighborsClassifier () knn.fit(X_train,y_train) y_knn = knn.predict(X_test) print('分类器得到的分类：\n ',y_knn) print('真实分类：\n ',y_test) print('准确率为：',accuracy_score(y_test,y_knn))采用随机森林模型来改写，用python

from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score # 加载鸢尾花数据集 iris = datasets.load_iris() iris...

from sklearn import datasets from sklearn.datasets import load_iris import numpy as np import math from sklearn.model_selection import train_test_split from collections import Counter from sklearn.linear_model import LogisticRegression #导入逻辑回归模型 #########Begin######## # 导入数据 iris = datasets.load_iris() X= iris['data'] y = iris['target'] X = X[y!=2] # 筛选数据，只选择标签为0和1 y=y[y!=2] # 数据划分 # 模型调用 # 模型训练 # 数据预测 # 结果打印 ########End#########

这段代码使用了scikit-learn库中的LogisticRegression模型对鸢尾花数据集做二分类预测。具体来说，代码首先导入了所需的库，包括数据集库、numpy库、math库、交叉验证库、计数器库以及逻辑回归库。然后从数据集中...

from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split iris_dataset = load_iris() #鸢尾花数据集随机拆分出训练集和测试集 x_train, x_test, y_train, y_test = train_test_split(______________________) #下面查看拆分后的训练集和测试集 print("x_train",x_train) #查看训练集数据 ______________________ #查看训练集分类结果 print("x_test",x_test) #查看测试集数据 print("y_test",y_test) #查看测试集分类结果 print("x_train shape: {}".format(x_train.shape)) #查看训练集大小 ____________________________________________ #查看测试集大小

from sklearn.model_selection import train_test_split iris_dataset = load_iris() # 鸢尾花数据集随机拆分出训练集和测试集 x_train, x_test, y_train, y_test = train_test_split(iris_dataset['data'], iris_...

from sklearn.datasets import load_iris from sklearn.tree import DecisionTreeClassifier from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score,confusion_matrix import numpy as np # 导入iris数据集 iris = load_iris() # 提取数据集中的特征数据 X = iris.data # 提取label 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,random_state=42,test_size=0.5,stratify=y) # 导入决策树，设置参数，最大深度为3，使用gini系数 tree = DecisionTreeClassifier(criterion='gini', max_depth=3, random_state=42) # 拟合训练集 tree.fit(X_train,y_train) # 预测测试集 y_predict = tree.predict(X_test) # 计算准确率 accuracy = accuracy_score(y_test,y_predict) # 混淆矩阵 cm = confusion_matrix(y_test,y_predict) TP = np.diag(cm) FN = cm.sum(axis=1) - np.diag(cm) FP = cm.sum(axis=0) - np.diag(cm) TN = cm.sum() - (TP+FN+FP) recall = TP / (TP+FN) # Overall Accuracy OA = (TP+TN) / (TP+FN+FP+TN) print(OA) # Average Accuracy AA = recall.sum() / len(recall) print(AA)逐句详细分析一下

from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score,confusion_matrix import numpy as np 首先导入需要的库，包括数据集iris、决策树分类器、数据集划分、...

# 1、获取数据集 from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.neighbors import KNeighborsClassifier from sklearn.model_selection import GridSearchCV iris = load_iris() #2、数据基本处理 --划分数据集 x_train, x_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2,random_state=22) #3、特征工程:标准化 #实例化一个转换器类 transfer = StandardScaler() #调用fit_transform x_train = transfer.fit_transform(x_train) x_test = transfer.transform(x_test) #4、KNN预估器流醒 #4.1 实例化预估器类 estimator = KNeighborsClassifier() #4.2模型选择与调优一一网络搜索和交叉验证 #准备要调的超参数 param_dict = {"n_neighbors": [1,3,5,7]} estimator = GridSearchCV(estimator, param_grid=param_dict, cv=4) #4.3 fit数据进行训练 estimator.fit(x_train, y_train) #5、评估模型效果 # 方法a:比对预测结果和真实值 y_predict = estimator.predict(x_test) print("比对预测结果和真实值:\n", y_predict == y_test) #方法b: 直接计算准确率 score = estimator.score(x_test,y_test) print("直接计算准确率:\n", score)

2. 使用load_iris()函数加载鸢尾花数据集，并使用train_test_split()函数将数据集划分为训练集和测试集。 3. 实例化StandardScaler()类，用于标准化数据。然后，分别使用fit_transform()函数对训练集和测试...

使用KNN分类器对sklearn中自带的make_blobs数据进行分类

1.使用make_blobs生成样本为200，分类为2的数据集，并绘制成...from sklearn.model_selection import train_test_split # 分类是判断样本属于那一类回归判断样本是一个连续值或者范围 # 生成样本是200 分类为2的数据集

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