xgboost predict_proba:如何在概率和标签之间进行映射 [英] xgboost predict_proba : How to do the mapping between the probabilities and the labels

问题描述

我正在尝试使用xgboost算法预测解决多类分类，但是我不知道 predict_proba 是如何工作的.实际上， predict_proba 会生成一个概率列表，但我不知道每种概率与哪个类别相关.

I'm trying to predict solve a multiclass classification using the xgboost algorithm, however i do not know how does predict_proba works exactly. In fact, predict_proba generates a list of probabilities but i don't know to which class each probability is related.

这是一个简单的例子:

这是我的火车数据:

+------------+----------+-------+
| feature1   | feature2 | label |
+------------+----------+-------+
|    x       |    z     |   3   |
+------------+----------+-------+
|    y       |    u     |   0   |
+------------+----------+-------+
|    x       |    u     |   2   |
+------------+----------+-------+

然后当我尝试预测probas的新例子

Then when I try to predict probas for a new example

model.predict_proba(['x','u'])

这将返回如下内容:

[0.2, 0.3, 0.5]

我的问题是:概率为0.5的类别是什么?是2类，3类还是0类?

My question is : what is the class that has the probability of 0.5 ? is it the class 2, or 3 or 0 ?

推荐答案

似乎您在使用xgboost的sklearn API.在这种情况下，模型具有专用的属性 model.classes _ ，该属性返回模型学习到的类，并且输出数组中类的顺序与概率的顺序相对应.

It seems that you use the sklearn API of xgboost. In this case the model has a dedicated attribute model.classes_ that returns the classes that were learned by the model and the order of classes in the output array corresponds to the order of probabilities.

这是一个有关伪数据的示例:

Here is an example with dummy data:

import numpy as np
import pandas as pd
import xgboost as xgb

# generate dummy data (10k examples, 10 numeric features, 4 classes of target)
np.random.seed(312)
train_X = np.random.random((10000,10))
train_y_mcc = np.random.randint(0, 4, train_X.shape[0]) #four classes:0,1,2,3

# model
xgb_model_mpg = xgb.XGBClassifier(max_depth= 3, n_estimators=100)
xgb_model_mpg.fit(train_X, train_y_mcc)

# classes
print(xgb_model_mpg.classes_)
>>> [0 1 2 3]

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