python中带有multioutputclassifier和xgboost的多标签分类示例

Scikit-learn API provides a MulitOutputClassifier class that helps to classify multi-output data. In this tutorial, we’ll learn how to classify multi-output (multi-label) data with this method in Python. Multi-output data contains more than one y label data for a given X input data. The tutorial covers:

Scikit-learn API提供了MulitOutputClassifier类，该类有助于对多输出数据进行分类。 在本教程中，我们将学习如何在Python中使用此方法对多输出(多标签)数据进行分类。 对于给定的X输入数据，多输出数据包含多个y标签数据。 本教程涵盖：

1. Preparing the data
   准备数据
2. Defining the model
   定义模型
3. Predicting and accuracy check
   预测和准确性检查
4. Source code listing
   源代码清单

我们将从加载本教程所需的库开始。(We’ll start by loading the required libraries for this tutorial.)

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)


from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
from sklearn.metrics import roc_auc_score
from sklearn.metrics import classification_report
from sklearn.datasets import make_multilabel_classification
from xgboost import XGBClassifier
from sklearn.model_selection import KFold
from sklearn.multioutput import MultiOutputClassifier
from sklearn.pipeline import Pipeline

准备数据(Preparing the data)

We can generate a multi-output data with a make_multilabel_classification function. The target dataset contains 20 features (x), 5 classes (y), and 10000 samples.

我们可以使用make_multilabel_classification函数生成多输出数据。 目标数据集包含20个要素(x)，5个类(y)和10000个样本。

We’ll define them in the parameters of the function.

我们将在函数的参数中定义它们。

x, y = make_multilabel_classification(n_samples=10000, n_features=20, n_classes=5, random_state=88)

The generated data looks as below. There are 20 features and 5 labels in this dataset.

生成的数据如下。 该数据集中有20个要素和5个标签。