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我试图使用 dask 来解决 kaggle 欺诈检测分类问题。但是，当我构建模型时，模型将所有值预测为 1。

我真的很惊讶，因为测试数据中有 56,000 个 zeor 和 92 个 zeor，但模型仍然以某种方式将所有值预测为 1。

我显然做错了什么。如何正确使用模型？

MWE

import numpy as np
import pandas as pd
import dask
import dask.dataframe as dd
import dask_ml
from dask_ml.xgboost import XGBClassifier
import collections
from dask_ml.model_selection import train_test_split
from dask.distributed import Client

# set up cluster
client = Client(n_workers=4)

# load the data
ifile = "https://github.com/vermaji333/MLProject/blob/master/creditcard.zip?raw=true"
#!wget https://github.com/vermaji333/MLProject/blob/master/creditcard.zip?raw=true
#ifile = 'creditcard.zip'
ddf = dd.read_csv(ifile,compression='zip',
                  blocksize=None,
                  assume_missing=True)

# train-test split
target = 'Class'

Xtr, Xtx, ytr, ytx = train_test_split(
    ddf.drop(target,axis=1), 
    ddf[target],
    test_size=0.2, 
    random_state=100,
    shuffle=True
)

# modelling
model = XGBClassifier(n_jobs=-1,
                      random_state=100,
                      scale_pos_weight=1, # default
                      objective='binary:logistic')
model.fit(Xtr,ytr)
ypreds = model.predict(Xtx)
ytx = ytx.compute()
ypreds = ypreds.compute()

# model evaluation
print(collections.Counter(ytx)) # Counter({0.0: 56607, 1.0: 92})
print(collections.Counter(ypreds)) # this gives all 1's

更新

我尝试了各种比例 pos 权重值。

I tried various scale_pos_weights
collections.Counter(ytr)
Counter({0.0: 227708, 1.0: 400})

scale_pos_weight= 227708/400
scale_pos_weight= 400/227708
scale_pos_weight= other values

But, for all parameters, I got all 1's as the result:

print(collections.Counter(ytx)) # Counter({0.0: 56607, 1.0: 92})
print(collections.Counter(ypreds)) # this gives all 1's
Counter({0.0: 56607, 1.0: 92})
Counter({1: 56699})
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