类型错误：__init__() 有一个意外的关键字参数"评分"

Question

如何处理此演示代码（取自此处：http://scikit-learn.org/dev/auto_examples/grid_search_digits.html)
当主观得分是一个参数时的TypeError: __init__() got an unexpected keyword argument 'scoring'(http://scikit-learn.org/dev/modules/generated/sklearn.grid_search.GridSearchCV.html#sklearn.grid_search.GridSearchCV)？

from __future__ import print_function
from sklearn import datasets
from sklearn.cross_validation import train_test_split
from sklearn.grid_search import GridSearchCV  
from sklearn.metrics import classification_report
from sklearn.svm import SVC
print(__doc__)
# Loading the Digits dataset
digits = datasets.load_digits()
# To apply an classifier on this data, we need to flatten the image, to
# turn the data in a (samples, feature) matrix:
n_samples = len(digits.images)
X = digits.images.reshape((n_samples, -1))
y = digits.target
# Split the dataset in two equal parts
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.5, random_state=0)
# Set the parameters by cross-validation
tuned_parameters = [{'kernel': ['rbf'], 'gamma': [1e-3, 1e-4],
                 'C': [1, 10, 100, 1000]},
                {'kernel': ['linear'], 'C': [1, 10, 100, 1000]}]
scores = ['precision', 'recall']
for score in scores:
    print("# Tuning hyper-parameters for %s" % score)
    print()
    clf = GridSearchCV(SVC(C=1), tuned_parameters, scoring=score)
    clf.fit(X_train, y_train, cv=5)
    print("Best parameters set found on development set:")
    print()
    print(clf.best_estimator_)
    print()
    print("Grid scores on development set:")
    print()
    for params, mean_score, scores in clf.grid_scores_:
        print("%0.3f (+/-%0.03f) for %r"
          % (mean_score, scores.std() / 2, params))
    print()
    print("Detailed classification report:")
    print()
    print("The model is trained on the full development set.")
    print("The scores are computed on the full evaluation set.")
    print()
    y_true, y_pred = y_test, clf.predict(X_test)
    print(classification_report(y_true, y_pred))
    print()
# Note the problem is too easy: the hyperparameter plateau is too flat and the
# output model is the same for precision and recall with ties in quality.

Answer 1

参数scoring在0.14开发版本中是新的，示例代码适用于该版本。您安装的scikit可能是0.13或更早版本，它没有评分参数。

Answer 2

您正在运行开发版本吗？

例如，0.12 中不支持该参数