SVM的标准化数据-Scikit learn/Python

Question

这适用于必须使用SVM方法来提高模型精度的分配。

共有3部分，编写了下面的代码

import sklearn.datasets as datasets
import sklearn.model_selection as ms
from sklearn.model_selection import train_test_split


digits = datasets.load_digits();
X = digits.data
y = digits.target

X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=30, stratify=y)

print(X_train.shape)
print(X_test.shape)

from sklearn.svm import SVC
svm_clf = SVC().fit(X_train, y_train)
print(svm_clf.score(X_test,y_test))

但在此之后，问题如下

执行数字标准化。数据，并将转换后的数据存储在可变数字中。

提示：从sklearn.preprocessing.使用所需的实用程序再次，将digits_standardized分成两个集合名称X_train和X_test。此外，将digits.target分成两组Y_train和Y_test。

提示：使用sklearn中的训练测试分割方法。模型选择；将随机_状态设置为30；并进行分层抽样。使用默认参数，从X_序列集和Y_序列标签构建另一个SVM分类器。将模型命名为svm_clf2。

在测试数据集上评估模型的准确性并打印其分数。

在上述代码的基础上，尝试编写此代码，但似乎失败了。有谁能帮助我们将数据标准化。

std_scale = preprocessing.StandardScaler().fit(X_train)
X_train_std = std_scale.transform(X_train)
X_test_std  = std_scale.transform(X_test)

svm_clf2 = SVC().fit(X_train, y_train)
print(svm_clf.score(X_test,y_test))

Answer 1

尝试此操作，因为最终代码包含所有任务

import sklearn.datasets as datasets
import sklearn.model_selection as ms
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC



digits = datasets.load_digits()
X = digits.data
y = digits.target

X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=30, stratify=y)

print(X_train.shape)
print(X_test.shape)

svm_clf = SVC().fit(X_train, y_train)
print(svm_clf.score(X_test,y_test))



scaler = StandardScaler()
scaler.fit(X)
digits_standardized = scaler.transform(X)

X_train, X_test, y_train, y_test = train_test_split(digits_standardized, y, random_state=30, stratify=y)


svm_clf2 = SVC().fit(X_train, y_train)
print(svm_clf2.score(X_test,y_test))

Answer 2

尝试了以下。似乎在工作。

import sklearn.datasets as datasets
import sklearn.model_selection as ms
from sklearn.model_selection import train_test_split

from sklearn.preprocessing import StandardScaler


digits = datasets.load_digits();


X = digits.data
scaler = StandardScaler()
scaler.fit(X)
digits_standardized = scaler.transform(X)

y = digits.target

X_train, X_test, y_train, y_test = train_test_split(digits_standardized, y, random_state=30, stratify=y)

#print(X_train.shape)
#print(X_test.shape)


from sklearn.svm import SVC
svm_clf2 = SVC().fit(X_train, y_train)
print("Accuracy ",svm_clf2.score(X_test,y_test))