Brush the Race tool, thank the people who share.
Summary
Recently played a variety of games, here to share some general Model, a little change can be used
Environment: Python 3.5.2
Xgboost: http://blog.csdn.net/han_xiaoyang/article/details/52665396
Xgboost Official API:
Http://xgboost.readthedocs.io/en/latest//python/python_api.htmlpreprocess[Python] View plain copy # Common preprocessing framework import pandas as PD import NumPy as NP import scipy as SP # file Read Def Read_csv_file (F, Logging=false): Print ("========== read Data =========") data = Pd.read_csv (f) If logging: Print (Data.head (5)) print (F, "contains the following") print (data.columns.values) print (data.describe ()) Print (Data.info ()) Return data
Logistic regression [python] View Plain copy # Universal logisticregression Framework Import pandas as pd import numpy as np from scipy import sparse from sklearn.preprocessing import onehotencoder from sklearn.linear_model import logisticregression from sklearn.preprocessing import standardscaler # 1. load data df_train =  PD. Dataframe () df_test = pd. Dataframe () y_train = df_train[' label '].values # 2. process data Ss = standardscaler () # 3. feature engineering/encoding # 3.1 for labeled feature Enc = onehotencoder () feats = ["CreativEID ", " Adid ", " Campaignid "] for i, feat in enumerate (feats): x_train = enc.fit_transform (Df_train[feat].values.reshape ( -1, 1)) x_test = enc.fit_transform (Df_test[feat].values.reshape (-1), 1))