datawhalechina / team-learning-data-mining Goto Github PK
View Code? Open in Web Editor NEW主要存储Datawhale组队学习中“数据挖掘/机器学习”方向的资料。
主要存储Datawhale组队学习中“数据挖掘/机器学习”方向的资料。
https://github.com/datawhalechina/team-learning-data-mining/blob/master/FinancialRiskControl/baseline.md
test = test_pred / kf.n_splits
在单次fold内对测试集的预测概率求平均?
code_flag应该是大于零,因为有的flag为2。
data_with_code.loc[data_with_code['code_flag'] > 0]
错误地址
#部分特征处理,取众数(首先去除缺失值的数据)
for i in range(1,9+1):
data.loc[data['public_service_'+str(i)]<0,'public_service_'+str(i)] = data['public_service'+str(i)].dropna().mode().values_
for i in range(1,13+1):
data.loc[data['trust_'+str(i)]<0,'trust_'+str(i)] = data['trust'+str(i)].dropna().mode().values_
解决方法
问题地址
2.3之后的所有图片相关maekdown语法地址引用错误,导致图片不能显示。
如2.3的第一个图片,应该为
![dscreteoverall](tupian/discreteoverall.PNG)
而不是
![dscreteoverall](discreteoverall.PNG)
![](tupian/%E6%AD%A3%E6%80%81%E5%88%86%E5%B8%83%E5%9B%BE.png)
而不是
![](%E6%AD%A3%E6%80%81%E5%88%86%E5%B8%83%E5%9B%BE.png)
有梯度下降的完整代码吗
data_train['employmentLength'].value_counts(dropna=False).sort_index()
When I run the following method, it raised error as stated at title
def make_zip(source_dir='./result/', output_filename = 'result.zip'):
zipf = zipfile.ZipFile(output_filename, 'w')
pre_len = len(os.path.dirname(source_dir))
source_dirs = os.walk(source_dir)
print(source_dirs)
for parent, dirnames, filenames in source_dirs:
print(parent, dirnames)
for filename in filenames:
if '.npy' not in filename:
continue
pathfile = os.path.join(parent, filename)
arcname = pathfile[pre_len:].strip(os.path.sep) #相对路径
zipf.write(pathfile, arcname)
zipf.close()
make_zip()
大佬讲的好棒,想请教您backward的算法和pytorch或者tensorflow或者opencv等等有关系吧?他们计算backward的算法有什么不同呀?
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