这里使用K近邻算法对生物物种进行分类。

导入数据

from sklearn.datasets import load_iris
iris = load_iris()
iris.data.shape

查看数据说明

print(iris.DESCR)

对数据集进行分割

from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.25, random_state=33)

对数据进行标准化处理

from sklearn.preprocessing import StandardScaler
ss = StandardScaler()
X_train = ss.fit_transform(X_train)
X_test = ss.transform(X_test)

通过KNN训练模型

from sklearn.neighbors import KNeighborsClassifier
knc = KNeighborsClassifier()
knc.fit(X_train, y_train)
y_predict = knc.predict(X_test)

准确性测评

print('The accuracy of K-Nearest Neighbor Classifier is', knc.score(X_test, y_test))

详细测评

from sklearn.metrics import classification_report
print(classification_report(y_test, y_predict, target_names=iris.target_names))