手写体数字预测

导入数据

# 从sklearn.datasets里导入手写体数字加载器。
from sklearn.datasets import load_digits
digits = load_digits()

数据分割及标准化

数据分割

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

数据标准化

# 从sklearn.preprocessing里导入数据标准化模块。
from sklearn.preprocessing import StandardScaler
from sklearn.svm import LinearSVC
ss = StandardScaler()
X_train = ss.fit_transform(X_train)
X_test = ss.transform(X_test)

模型训练

# 初始化线性假设的支持向量机分类器LinearSVC。
lsvc = LinearSVC()
lsvc.fit(X_train, y_train)
y_predict = lsvc.predict(X_test)
print(y_predict)

 预测性能评分

# 查看评分
lsvc.score(X_test, y_test)
from sklearn.metrics import classification_report
print(classification_report(y_test, y_predict, target_names=digits.target_names.astype(str)))

评分如图1-1

 　　　　　图1-1 支持向量机模型评分