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

应填写的内容如下：

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(iris_dataset['data'], iris_dataset['target'], random_state=0)

# 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 
print("y_train",y_train)                         #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 
print("x_test shape: {}".format(x_test.shape))   #查看测试集大小

相关问题

import numpy as np import matplotlib.pyplot as plt from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split from sklearn.svm import SVC from sklearn.metrics import accuracy_score # 加载鸢尾花数据集 iris = load_iris() X = iris.data y = iris.target # 数据可视化 - 处理前 plt.scatter(X[:, 0], X[:, 1], c=y) plt.xlabel('Sepal Length') plt.ylabel('Sepal Width') plt.title('Iris Dataset - Before Preprocessing') plt.show() # 划分数据集为训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # 创建并拟合支持向量机模型 model = SVC() model.fit(X_train, y_train) # 在测试集上进行预测 y_pred = model.predict(X_test) # 计算准确度 accuracy = accuracy_score(y_test, y_pred) print('Accuracy:', accuracy) # 数据可视化 - 处理后 plt.scatter(X_test[:, 0], X_test[:, 1], c=y_pred) plt.xlabel('Sepal Length') plt.ylabel('Sepal Width') plt.title('Iris Dataset - After Preprocessing') plt.show()

这段代码使用了支持向量机（SVM）算法对鸢尾花数据集进行分类，并且使用了数据可视化来展示分类效果。具体来说，代码中首先加载了鸢尾花数据集，然后对数据集进行了可视化处理，接着将数据集划分为训练集和测试集，使用SVM算法对训练集进行拟合，并在测试集上进行预测，最后计算了模型的准确度，并使用数据可视化展示了预测结果。

import numpy as np from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split iris_dataset = load_iris() X_train, X_test, y_train, y_test = train_test_split( iris_dataset['data'], iris_dataset['target'], random_state=0 ) from sklearn.neighbors import KNeighborsClassifier # SciPy1.10 及以上版本不允许 scipy.stats.mode(a, axis=0, nan_policy='propagate', keepdims=None) 中的 keepdims 为 None # 而 KNeighborClassifier 中默认的 weights 为 uniform , uniform 中用到了 stats.mode knn_neighbors_1 = KNeighborsClassifier(n_neighbors=1, weights='distance') knn_neighbors_2 = KNeighborsClassifier(n_neighbors=11, weights='distance') knn_neighbors_3 = KNeighborsClassifier(n_neighbors=37) knn_neighbors_1.fit(X_train, y_train) knn_neighbors_2.fit(X_train, y_train) knn_neighbors_3.fit(X_train, y_train) print("Test set score for neighbors=1 and weights='distance': {:.2f}".format(knn_neighbors_1.score(X_test, y_test))) print("Test set score for neighbors=37 and metric='manhattan': {:.2f}".format(knn_neighbors_2.score(X_test, y_test))) print("Test set score for neighbors=37: {:.2f}".format(knn_neighbors_3.score(X_test, y_test)))

以上代码是使用Python语言中的Scikit-learn库对鸢尾花数据集进行操作的代码。其中，Numpy和Scikit-learn中的Iris数据集被导入，然后使用train_test_split功能对数据集进行分类和拆分，最后用K最近邻分类器对数据集进行分类。