一维数组上的SVM

Question

我正在处理Titanic数据集。我正在尝试使用以下代码将SVM应用于许多单独功能：

quanti_vars = ['Age','Pclass','Fare','Parch']

imp_med = SimpleImputer(missing_values=np.nan, strategy='median')
imp_med.fit(titanic[['Age']])

for i in (X_train, X_test):
    i[['Age']] = imp_med.transform(i[['Age']])

svm_clf = SVC()
svm_clf.fit(X_train[quanti_vars], y_train)
y_pred = svm_clf.predict(X_test[quanti_vars])
svm_accuracy = accuracy_score(y_pred, y_test)
svm_accuracy

for i in quanti_vars:
    svm_clf.fit(X_train[i], y_train)
    y_pred = svm_clf.predict(X_test[i])
    svm_accuracy = accuracy_score(y_pred, y_test)
    print(i,': ',svm_accuracy)

那个最后的for循环抛出ValueError: Expected 2D array, got 1D array instead错误，我不知道为什么-SVM不能在单个功能上运行吗？

Answer 1

我意识到，很简单，我需要将i放在括号中以正确地子集化。因此：

for i in quanti_vars:
    svm_clf.fit(X_train[[i]], y_train)
    y_pred = svm_clf.predict(X_test[[i]])
    svm_accuracy = accuracy_score(y_pred, y_test)
    print(i,': ',svm_accuracy)

产生

SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
  decision_function_shape='ovr', degree=3, gamma='auto_deprecated',
  kernel='rbf', max_iter=-1, probability=False, random_state=None,
  shrinking=True, tol=0.001, verbose=False)
Age :  0.5874125874125874
SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
  decision_function_shape='ovr', degree=3, gamma='auto_deprecated',
  kernel='rbf', max_iter=-1, probability=False, random_state=None,
  shrinking=True, tol=0.001, verbose=False)
Pclass :  0.5874125874125874
SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
  decision_function_shape='ovr', degree=3, gamma='auto_deprecated',
  kernel='rbf', max_iter=-1, probability=False, random_state=None,
  shrinking=True, tol=0.001, verbose=False)
Fare :  0.42657342657342656
SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
  decision_function_shape='ovr', degree=3, gamma='auto_deprecated',
  kernel='rbf', max_iter=-1, probability=False, random_state=None,
  shrinking=True, tol=0.001, verbose=False)
Parch :  0.6153846153846154

（（我不会假装它很好，但是至少它能起作用。）

Answer 2

很简单只需写成这样：

y_pred = svm_clf.predict([X_test[i]])

添加[]会将其转换为2D数组