我已經定義了一個二進制分類器作爲波紋管:我用'gbc'方法(梯度提升分類器)調用它,並且我得到錯誤min_samples_split must be at least 2 or in (0, 1], got 1
最後一行featuresClasses是一個數據幀,並且featureLabels是功能列表min_samples_split必須至少爲2或在(0,1]中,得到1
Binary_classifier(method, featureLabels, featuresClasses):
membershipIds = list(set(featuresClasses['membershipId']))
n_membershipIds = len(membershipIds)
index_rand = np.random.permutation(n_membershipIds)
test_size = int(0.3 * n_membershipIds)
membershipIds_test = list(itemgetter(*index_rand[:test_size])(membershipIds))
membershipIds_train = list(itemgetter(*index_rand[test_size+1:])(membershipIds))
data_test = featuresClasses[featuresClasses['membershipId'].isin(membershipIds_test)]
data_train = featuresClasses[featuresClasses['membershipId'].isin(membershipIds_train)]
data_test = data_test[data_test['standing'].isin([0, 1])]
data_train = data_train[data_train['standing'].isin([0, 1])]
X_test = data_test[featureLabels].as_matrix()
y_test = data_test['standing'].values.astype(int)
X_train = data_train[featureLabels].as_matrix()
y_train = data_train['standing'].values.astype(int)
# -------------------------- Run classifier
print 'Binary classification by', method
if method == 'svm':
classifier = svm.SVC(kernel='linear', probability=True)
y_score = classifier.fit(X_train, y_train).decision_function(X_test)
elif method == 'gbc':
params = {'n_estimators': 200, 'max_depth': 3, 'min_samples_split': 1, 'learning_rate': 0.1, 'loss': 'deviance'}
classifier = GradientBoostingClassifier(**params)
y_score = classifier.fit(X_train, y_train).predict(X_test)
謝謝@Vivek庫馬爾 – YNr