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我对 sklearn Gradient Boosting Classifier 的 estimator.loss_ 方法有疑问。我试图将测试错误与一段时间内的训练错误进行比较。这是我的一些数据准备：

# convert data to numpy array
train = np.array(shuffled_ds)

#label encode neighborhoods
for i in range(train.shape[1]):
if i in [1,2]:
    print(i,list(train[1:5,i]))
    lbl = preprocessing.LabelEncoder()
    lbl.fit(list(train[:,i]))
    train[:,i] = lbl.transform(train[:,i])
print('neighborhoods & crimes encoded')

#create target vector
y_crimes = train[::,1]
train=np.delete(train,1,1)
print(y_crimes)

#arrays to float
train = train.astype(float)
y_crimes = y_crimes.astype(float)

#data holdout for testing
X_train, X_test, y_train, y_test = cross_validation.train_test_split(
    train, y_crimes, test_size=0.4, random_state=0)
print('test data created')

#train model and check train vs test error
print('begin training...')
est=GBC(n_estimators = 3000,learning_rate=.1,max_depth=4,max_features=1,min_samples_leaf=3)
est.fit(X_train,y_train)
print('done training')

此时，当我打印出我的数组形状时

print(X_train.shape)
print(X_test.shape)
print(y_train.shape)
print(y_test.shape)

我得到：

(18000, 9)
(12000, 9)
(18000,)
(12000,)

分别。

所以我的形状根据 sklearn 文档是兼容的。但接下来，我尝试填充一个测试分数向量，以便将其与我的训练错误进行比较，如下所示：

test_score=np.empty(len(est.estimators_))
for i, pred in enumerate(est.staged_predict(X_test)):
    test_score[i] = est.loss_(y_test,pred)

我收到以下错误：

: operands could not be broadcast together with shapes (12000,47) (12000,) 
         return np.sum(-1 * (Y * pred).sum(axis=1) +
543    544else:ValueError

我不确定 47 是从哪里来的。我之前在另一个数据集上使用过相同的程序并且没有问题。任何帮助将非常感激。

4

1 回答 1

0

您发出此错误是因为您必须将 staged_decision_function（而不是 staged_predict）方法的结果传递给 loss_

看这里Gradient Boosting 正则化

clf = ensemble.GradientBoostingClassifier(**params)
clf.fit(X_train, y_train)

# compute test set deviance
test_deviance = np.zeros((params['n_estimators'],), dtype=np.float64)

for i, y_pred in enumerate(clf.staged_decision_function(X_test)):
    # clf.loss_ assumes that y_test[i] in {0, 1}
    test_deviance[i] = clf.loss_(y_test, y_pred)
于 2015-09-02T20:32:38.853 回答