LightGBM的多类别分类 [英] Multiclass Classification with LightGBM

问题描述

我正在尝试使用Python中的LightGBM为多类分类问题(3个类)建模分类器.我使用了以下参数.

I am trying to model a classifier for a multi-class Classification problem (3 Classes) using LightGBM in Python. I used the following parameters.

params = {'task': 'train',
    'boosting_type': 'gbdt',
    'objective': 'multiclass',
    'num_class':3,
    'metric': 'multi_logloss',
    'learning_rate': 0.002296,
    'max_depth': 7,
    'num_leaves': 17,
    'feature_fraction': 0.4,
    'bagging_fraction': 0.6,
    'bagging_freq': 17}

数据集的所有分类特征均使用LabelEncoder进行标签编码.如下所示，我在用eartly_stopping运行cv后训练了模型.

All the categorical features of the dataset is label encoded with LabelEncoder. I trained the model after running cv with eartly_stopping as shown below.

lgb_cv = lgbm.cv(params, d_train, num_boost_round=10000, nfold=3, shuffle=True, stratified=True, verbose_eval=20, early_stopping_rounds=100)

nround = lgb_cv['multi_logloss-mean'].index(np.min(lgb_cv['multi_logloss-mean']))
print(nround)

model = lgbm.train(params, d_train, num_boost_round=nround)

训练后，我用这样的模型进行预测

After training, I made prediction with model like this,

preds = model.predict(test)
print(preds)             

我得到了这样的嵌套数组作为输出.

I got a nested array as output like this.

[[  7.93856847e-06   9.99989550e-01   2.51164967e-06]
 [  7.26332978e-01   1.65316511e-05   2.73650491e-01]
 [  7.28564308e-01   8.36756769e-06   2.71427325e-01]
 ..., 
 [  7.26892634e-01   1.26915179e-05   2.73094674e-01]
 [  5.93217601e-01   2.07172044e-04   4.06575227e-01]
 [  5.91722491e-05   9.99883828e-01   5.69994435e-05]]

由于preds中的每个列表都代表了类概率，因此我使用np.argmax()来找到这样的类.

As each list in the preds represent the class probabilites I used np.argmax() to find the classes like this..

predictions = []

for x in preds:
    predictions.append(np.argmax(x))

在分析预测时，我发现我的预测仅包含2类-0和1.2类是训练集中的第二大类，但是在预测中却找不到.给出了78%的准确性.

While analyzing the prediction I found that my predictions contain only 2 classes - 0 and 1. Class 2 was the 2nd largest class in the training set, but it was nowhere to be found in the predictions.. On evaluating the result it gave about 78% accuracy.

那么，为什么我的模型没有针对任何情况预测2类?我使用的参数有什么问题吗?

So, why didn't my model predict class 2 for any of the cases.? Is there anything wrong in the parameters I used.?

这不是解释模型做出的预测的正确方法吗?我应该对参数进行任何更改吗??

Isn't this the proper way to make interpret prediction made by the model.? Should I make any changes for the parameters.??

推荐答案

尝试通过交换类0和2，并重新运行训练和预测过程来进行故障排除.

Try troubleshooting by swapping classes 0 and 2, and re-running the trainining and prediction process.

如果新的预测仅包含1类和2类(很可能是根据您提供的数据得出的):

If the new predictions only contain classes 1 and 2 (most likely given your provided data):

• 分类器可能没有学过第三堂课；也许其功能与较大类的功能重叠，并且分类器默认使用较大类，以最大程度地减少目标函数.尝试提供平衡的训练集(每个班级有相同数量的样本)，然后重试.

如果新的预测确实包含所有3个类别:

If the new predictions do contain all 3 classes:

• 您的代码中某处出了点问题.需要更多信息来确定到底出了什么问题.

希望这会有所帮助.

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