|
|
马上注册,结交更多好友,享用更多功能^_^
您需要 登录 才可以下载或查看,没有账号?立即注册
x
- import pandas as pd
- from sklearn import svm
- import numpy as np
- from sklearn import datasets
- from sklearn.model_selection import train_test_split # 引入测试集,训练集
- from sklearn.metrics import accuracy_score # 引入准确率
- data= datasets.load_iris()
- print (data)
- # 将文件中非数字数据转换为数字
- data.loc[data["Iris_type"] == "Iris-setosa", "Iris_type"] = 0
- data.loc[data["Iris_type"] == "Iris-versicolor", "Iris_type"] = 1
- data.loc[data["Iris_type"] == "Iris-virginica", "Iris_type"] = 2
- iris = np.array(data, dtype=np.float64) # 生成矩阵
- print (iris)
- X = iris[:, 0:-1] # 提取features
- print (X)
- y = iris[:, -1].astype(int) # 提取labels
- print (y)
- X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0) # 随机划分训练集、测试集
- clf = svm.SVC()
- clf.fit(X_train, y_train)
- y_pred = clf.predict(X_test)
- accuracy_score(y_test, y_pred)
- Accuracy = accuracy_score(y_test, y_pred)
- print("%.2f%%" % (Accuracy * 100)) # 以百分数的形式,打印出准确率
复制代码
我是想利用樱尾花的数据集进行分析的但是出现了下面的错误
|
-
这是代码运行的结果
|