用于多标签分类的 XGBoost? [英] XGBoost for multilabel classification?

问题描述

是否可以使用 XGBoost 进行多标签分类?现在我使用 OneVsRestClassifier 而不是 sklearn 的 GradientBoostingClassifier.它可以工作，但只使用我 CPU 的一个内核.在我的数据中，我有大约 45 个特征，任务是用二进制(布尔)数据预测大约 20 列.指标是平均精度 (map@7).如果您有一个简短的代码示例要分享，那就太好了.

Is it possible to use XGBoost for multi-label classification? Now I use OneVsRestClassifier over GradientBoostingClassifier from sklearn. It works, but use only one core from my CPU. In my data I have ~45 features and the task is to predict about 20 columns with binary (boolean) data. Metric is mean average precision (map@7). If you have a short example of code to share, that would be great.

推荐答案

一种可能的方法，而不是使用用于多类任务的 OneVsRestClassifier，是使用 MultiOutputClassifier> 来自 sklearn.multioutput 模块.

One possible approach, instead of using OneVsRestClassifier which is for multi-class tasks, is to use MultiOutputClassifier from the sklearn.multioutput module.

下面是一个小的可重现的示例代码，其中包含 OP 请求的输入特征和目标输出的数量

Below is a small reproducible sample code with the number of input features and target outputs requested by the OP

import xgboost as xgb
from sklearn.datasets import make_multilabel_classification
from sklearn.model_selection import train_test_split
from sklearn.multioutput import MultiOutputClassifier
from sklearn.metrics import accuracy_score

# create sample dataset
X, y = make_multilabel_classification(n_samples=3000, n_features=45, n_classes=20, n_labels=1,
                                      allow_unlabeled=False, random_state=42)

# split dataset into training and test set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123)

# create XGBoost instance with default hyper-parameters
xgb_estimator = xgb.XGBClassifier(objective='binary:logistic')

# create MultiOutputClassifier instance with XGBoost model inside
multilabel_model = MultiOutputClassifier(xgb_estimator)

# fit the model
multilabel_model.fit(X_train, y_train)

# evaluate on test data
print('Accuracy on test data: {:.1f}%'.format(accuracy_score(y_test, multilabel_model.predict(X_test))*100))

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