标签： category cols train print data ###

数据前处理

1. 导入数据

   import pandas as pd
   import numpy as np
   from sklearn.cross_validation import train_test_split
   ### Load data
   ### Split the data to train and test sets
   data = pd.read_csv('data/loan/Train.csv', encoding = "ISO-8859-1")
   train, test = train_test_split(data,train_size=0.7,random_state=123,stratify=data['Disbursed'])
   ### Check number of nulls in each feature column
   nulls_per_column = train.isnull().sum()
   print(nulls_per_column)

   View Code
2. 将特征拆分成数值型和种类型

   ### Drop the useless columns
   train_1 = train.drop(['ID','Lead_Creation_Date','LoggedIn'],axis=1)
   ### Split the columns to numerical and categorical
   category_cols = train_1.columns[train_1.dtypes==object].tolist()
   category_cols.remove('DOB')
   category_cols.append('Var4')
   numeric_cols = list(set(train_1.columns)-set(category_cols))

   View Code
3. 分析并处理种类型特征

   ### explore the categorical columns
   for v in category_cols:
       print('Ratio of missing value for variable {0}: {1}'.format(v,nulls_per_column[v]/train_1.shape[0]))
   print('-----------------------------------------------------------')
   counts = dict()
   for v in category_cols:
       print('\nFrequency count for variable %s'%v)
       counts[v] = train_1[v].value_counts()
       print(counts[v])
   ### merge the cities that counts<200
   merge_city = [c for c in counts['City'].index if counts['City'][c]<200]
   train_1['City'] = train_1['City'].apply(lambda x: 'others' if x in merge_city else x)
   ### merge the salary accounts that counts<100
   merge_sa = [c for c in counts['Salary_Account'].index if counts['Salary_Account'][c]<100]
   train_1['Salary_Account'] = train_1['Salary_Account'].apply(lambda x: 'others' if x in merge_sa else x)
   ### merge the sources that counts<100
   merge_sr = [c for c in counts['Source'].index if counts['Source'][c]<100]
   train_1['Source'] = train_1['Source'].apply(lambda x: 'others' if x in merge_sr else x)
   ### impute the missing value
   train_1['City'].fillna('Missing',inplace=True)
   train_1['Salary_Account'].fillna('Missing',inplace=True)
   ### delete the column Employer_Name since too many categories
   train_2 = train_1.drop('Employer_Name',axis=1)

   View Code
4. 分析并处理数值型特征
5.  One-Hot encoding

模型调参

1. 建立基础模型并使用early_stop调整迭代次数
2. Tune max_depth and min_child_weight
3. Tune gamma
4. Tune subsample and colsample_bytree
5. Tune reg_alpha
6. Tune reg_lambda
7. Reduce learning rate

标签：,category,cols,train,print,data,###
来源： https://www.cnblogs.com/sunwq06/p/10793016.html

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