<p>我意识到,非常简单,我需要将<code>i</code>放在两个括号中以正确地表示子集。因此:</p>
<pre><code>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)
</code></pre>
<p>产生</p>
<pre><code>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
</code></pre>
<p>(我不会假装它很好,但至少它起作用了。)</p>