pingzishinee

XGboost模型训练与调参

XGboost

XGboost是（Gradient Boosting Decision Tree）梯度提升树的一种实现。
DBDT Model cycle:
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Example

import pandas as pd

data= pd.read_csv(r'G:\kaggle\housePrice\train.csv')

data.head()

	Id	MSSubClass	MSZoning	LotFrontage	LotArea	Street	Alley	LotShape	LandContour	Utilities	...	PoolQC	Fence	MiscFeature	MoSold	YrSold	SaleType	SaleCondition	SalePrice
0	1	60	RL	65.0	8450	Pave	NaN	Reg	Lvl	AllPub	...	NaN	NaN	NaN	2	2008	WD	Normal	208500
1	2	20	RL	80.0	9600	Pave	NaN	Reg	Lvl	AllPub	...	NaN	NaN	NaN	5	2007	WD	Normal	181500
2	3	60	RL	68.0	11250	Pave	NaN	IR1	Lvl	AllPub	...	NaN	NaN	NaN	9	2008	WD	Normal	223500
3	4	70	RL	60.0	9550	Pave	NaN	IR1	Lvl	AllPub	...	NaN	NaN	NaN	2	2006	WD	Abnorml	140000
4	5	60	RL	84.0	14260	Pave	NaN	IR1	Lvl	AllPub	...	NaN	NaN	NaN	12	2008	WD	Normal	250000

5 rows × 81 columns

data.describe()

	Id	MSSubClass	LotFrontage	LotArea	OverallQual	OverallCond	YearBuilt	YearRemodAdd	MasVnrArea	BsmtFinSF1	...	WoodDeckSF	OpenPorchSF	EnclosedPorch	3SsnPorch	ScreenPorch	PoolArea	MiscVal	MoSold	YrSold	SalePrice
count	1460.000000	1460.000000	1201.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1452.000000	1460.000000	...	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000
mean	730.500000	56.897260	70.049958	10516.828082	6.099315	5.575342	1971.267808	1984.865753	103.685262	443.639726	...	94.244521	46.660274	21.954110	3.409589	15.060959	2.758904	43.489041	6.321918	2007.815753	180921.195890
std	421.610009	42.300571	24.284752	9981.264932	1.382997	1.112799	30.202904	20.645407	181.066207	456.098091	...	125.338794	66.256028	61.119149	29.317331	55.757415	40.177307	496.123024	2.703626	1.328095	79442.502883
min	1.000000	20.000000	21.000000	1300.000000	1.000000	1.000000	1872.000000	1950.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	2006.000000	34900.000000
25%	365.750000	20.000000	59.000000	7553.500000	5.000000	5.000000	1954.000000	1967.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	5.000000	2007.000000	129975.000000
50%	730.500000	50.000000	69.000000	9478.500000	6.000000	5.000000	1973.000000	1994.000000	0.000000	383.500000	...	0.000000	25.000000	0.000000	0.000000	0.000000	0.000000	0.000000	6.000000	2008.000000	163000.000000
75%	1095.250000	70.000000	80.000000	11601.500000	7.000000	6.000000	2000.000000	2004.000000	166.000000	712.250000	...	168.000000	68.000000	0.000000	0.000000	0.000000	0.000000	0.000000	8.000000	2009.000000	214000.000000
max	1460.000000	190.000000	313.000000	215245.000000	10.000000	9.000000	2010.000000	2010.000000	1600.000000	5644.000000	...	857.000000	547.000000	552.000000	508.000000	480.000000	738.000000	15500.000000	12.000000	2010.000000	755000.000000

8 rows × 38 columns

data.SalePrice.isnull().sum() #SalePrice列没有缺失值

data.dropna(axis=0, subset=['SalePrice'], inplace= True)
#pandas中很多函数都会有inplace参数
#inplace：是否就地修改（直接对原对象修改）
#inplace= True：直接就地修改
#默认inplace为False，即创建新的对象进行修改，原对象不变

#subset:在哪些列中查看有缺失值

data.describe()

	Id	MSSubClass	LotFrontage	LotArea	OverallQual	OverallCond	YearBuilt	YearRemodAdd	MasVnrArea	BsmtFinSF1	...	WoodDeckSF	OpenPorchSF	EnclosedPorch	3SsnPorch	ScreenPorch	PoolArea	MiscVal	MoSold	YrSold	SalePrice
count	1460.000000	1460.000000	1201.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1452.000000	1460.000000	...	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000
mean	730.500000	56.897260	70.049958	10516.828082	6.099315	5.575342	1971.267808	1984.865753	103.685262	443.639726	...	94.244521	46.660274	21.954110	3.409589	15.060959	2.758904	43.489041	6.321918	2007.815753	180921.195890
std	421.610009	42.300571	24.284752	9981.264932	1.382997	1.112799	30.202904	20.645407	181.066207	456.098091	...	125.338794	66.256028	61.119149	29.317331	55.757415	40.177307	496.123024	2.703626	1.328095	79442.502883
min	1.000000	20.000000	21.000000	1300.000000	1.000000	1.000000	1872.000000	1950.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	2006.000000	34900.000000
25%	365.750000	20.000000	59.000000	7553.500000	5.000000	5.000000	1954.000000	1967.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	5.000000	2007.000000	129975.000000
50%	730.500000	50.000000	69.000000	9478.500000	6.000000	5.000000	1973.000000	1994.000000	0.000000	383.500000	...	0.000000	25.000000	0.000000	0.000000	0.000000	0.000000	0.000000	6.000000	2008.000000	163000.000000
75%	1095.250000	70.000000	80.000000	11601.500000	7.000000	6.000000	2000.000000	2004.000000	166.000000	712.250000	...	168.000000	68.000000	0.000000	0.000000	0.000000	0.000000	0.000000	8.000000	2009.000000	214000.000000
max	1460.000000	190.000000	313.000000	215245.000000	10.000000	9.000000	2010.000000	2010.000000	1600.000000	5644.000000	...	857.000000	547.000000	552.000000	508.000000	480.000000	738.000000	15500.000000	12.000000	2010.000000	755000.000000

8 rows × 38 columns

#target
y= data['SalePrice']

#X
#并除去非数值列
X= data.drop(['SalePrice'], axis=1).select_dtypes(exclude=['object'])

#缺失值处理——impute法
from sklearn.impute import SimpleImputer

my_imputer= SimpleImputer()#missing_values=np.NaN, strategy='mean'
X_with_imputed_values= my_imputer.fit_transform(X)

#split
from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test= train_test_split( X_with_imputed_values, y, test_size=0.33)

#model
from xgboost import XGBRegressor  #注意：是小写的xgb
model= XGBRegressor()
model

XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,
       colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0,
       max_depth=3, min_child_weight=1, missing=None, n_estimators=100,
       n_jobs=1, nthread=None, objective='reg:linear', random_state=0,
       reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,
       silent=True, subsample=1)

#fit
clf= model.fit(X_train, y_train)

#evaluation1
clf.score(X_test, y_test)

0.842475147639184

#make predictions
predictions= clf.predict(X_test)

#evlaustion2
from sklearn.metrics import mean_absolute_error

mean_absolute_error(predictions, y_test)

17306.50893088693

Model Tuning模型调整

调两个重要的参数：n_estimators and early_stopping_rounds

n_estimators:
上面描述的modeling cycle的次数, 即总共迭代的次数，也即决策树的个数（如果选择的基学习器是决策树）
太小易underfitting
太大易overfitting
typical value range 100-1000, 这取决于下面讨论的learning rate。

early_stopping_rounds:
早停策略
early_stopping_rounds=5 #如果验证集eval_set的score在5轮内都没有提高（或者误差没有降低），则提前停止迭代，结束模型训练。
调参：防止过拟合
set a high value for n_estimators and then use early_stopping_rounds to find the optimal time to stop iterating是一个明智之举。

improved_model= XGBRegressor(booster='gbtree', #gbtree树模型做基学习器（默认）  booster='gbliner'线性模型做基学习器
                             n_estimators=700, #700个基学习器，迭代700轮（fit中展示训练情况，然后不断试）
                             silent=True,       #不输出中间结果（默认）,沉默嘛
                             learning_rate=0.1) #学习率（步长）  默认0.1 
                         
improved_model.fit(X_train, y_train, 
                   #early_stopping_rounds=10,#10次误差不下降了，停止在78次迭代，But再试着继续迭代，后面误差还是会降低？？？？？？？SO这个怎么用？
                   eval_set=[(X_test, y_test)],  #验证集，还得加个()
                   verbose= True) #展示validation metrics，输出到终端 如本次的rmse值

[0]	validation_0-rmse:171354
[1]	validation_0-rmse:155035
[2]	validation_0-rmse:140105
[3]	validation_0-rmse:127156
[4]	validation_0-rmse:115501
[5]	validation_0-rmse:104982
[6]	validation_0-rmse:95640.1
[7]	validation_0-rmse:87385.7
[8]	validation_0-rmse:79873.8
[9]	validation_0-rmse:73282.3
[10]	validation_0-rmse:67515.6
[11]	validation_0-rmse:62389.5
[12]	validation_0-rmse:57831.5
[13]	validation_0-rmse:53889.4
[14]	validation_0-rmse:50267.4
[15]	validation_0-rmse:47156
[16]	validation_0-rmse:44856.7
[17]	validation_0-rmse:42583.1
[18]	validation_0-rmse:40623.5
[19]	validation_0-rmse:38994.8
[20]	validation_0-rmse:37508.2
[21]	validation_0-rmse:36328.1
[22]	validation_0-rmse:35230.5
[23]	validation_0-rmse:34482.7
[24]	validation_0-rmse:33812.3
[25]	validation_0-rmse:33117.6
[26]	validation_0-rmse:32555.1
[27]	validation_0-rmse:32110.1
[28]	validation_0-rmse:31723.5
[29]	validation_0-rmse:31453.6
[30]	validation_0-rmse:31251.2
[31]	validation_0-rmse:31114.1
[32]	validation_0-rmse:30839.7
[33]	validation_0-rmse:30671.4
[34]	validation_0-rmse:30502.4
[35]	validation_0-rmse:30366.8
[36]	validation_0-rmse:30261.1
[37]	validation_0-rmse:30134.2
[38]	validation_0-rmse:30074.4
[39]	validation_0-rmse:29955.6
[40]	validation_0-rmse:29821.9
[41]	validation_0-rmse:29768.3
[42]	validation_0-rmse:29677.8
[43]	validation_0-rmse:29747
[44]	validation_0-rmse:29762.3
[45]	validation_0-rmse:29687.5
[46]	validation_0-rmse:29597.7
[47]	validation_0-rmse:29572.5
[48]	validation_0-rmse:29499.4
[49]	validation_0-rmse:29490.6
[50]	validation_0-rmse:29431.2
[51]	validation_0-rmse:29456.1
[52]	validation_0-rmse:29418.4
[53]	validation_0-rmse:29429.7
[54]	validation_0-rmse:29420.7
[55]	validation_0-rmse:29352.5
[56]	validation_0-rmse:29334.4
[57]	validation_0-rmse:29327
[58]	validation_0-rmse:29353.8
[59]	validation_0-rmse:29313.8
[60]	validation_0-rmse:29265
[61]	validation_0-rmse:29251.9
[62]	validation_0-rmse:29278.7
[63]	validation_0-rmse:29232.1
[64]	validation_0-rmse:29241.9
[65]	validation_0-rmse:29225
[66]	validation_0-rmse:29224.7
[67]	validation_0-rmse:29276.3
[68]	validation_0-rmse:29296.9
[69]	validation_0-rmse:29268.3
[70]	validation_0-rmse:29243.8
[71]	validation_0-rmse:29254.7
[72]	validation_0-rmse:29268.9
[73]	validation_0-rmse:29244.3
[74]	validation_0-rmse:29253.9
[75]	validation_0-rmse:29198.6
[76]	validation_0-rmse:29186.1
[77]	validation_0-rmse:29192
[78]	validation_0-rmse:29131.9
[79]	validation_0-rmse:29134
[80]	validation_0-rmse:29154.9
[81]	validation_0-rmse:29162.2
[82]	validation_0-rmse:29143.9
[83]	validation_0-rmse:29154
[84]	validation_0-rmse:29169.5
[85]	validation_0-rmse:29174.1
[86]	validation_0-rmse:29178.5
[87]	validation_0-rmse:29184.7
[88]	validation_0-rmse:29194.5
[89]	validation_0-rmse:29174.5
[90]	validation_0-rmse:29147
[91]	validation_0-rmse:29153.5
[92]	validation_0-rmse:29115.3
[93]	validation_0-rmse:29092.4
[94]	validation_0-rmse:29096.9
[95]	validation_0-rmse:29049.2
[96]	validation_0-rmse:29035.4
[97]	validation_0-rmse:29027.6
[98]	validation_0-rmse:29034.5
[99]	validation_0-rmse:29029.3
[100]	validation_0-rmse:29014.2
[101]	validation_0-rmse:29019.8
[102]	validation_0-rmse:29032.2
[103]	validation_0-rmse:29058.4
[104]	validation_0-rmse:29050.9
[105]	validation_0-rmse:29044.3
[106]	validation_0-rmse:29011.1
[107]	validation_0-rmse:29000.5
[108]	validation_0-rmse:28990
[109]	validation_0-rmse:28989.8
[110]	validation_0-rmse:28997.3
[111]	validation_0-rmse:28955.4
[112]	validation_0-rmse:28962.5
[113]	validation_0-rmse:29004.7
[114]	validation_0-rmse:28986.9
[115]	validation_0-rmse:28965
[116]	validation_0-rmse:28934.8
[117]	validation_0-rmse:28924.8
[118]	validation_0-rmse:28922
[119]	validation_0-rmse:28897
[120]	validation_0-rmse:28878.8
[121]	validation_0-rmse:28889.1
[122]	validation_0-rmse:28970.5
[123]	validation_0-rmse:28983.8
[124]	validation_0-rmse:28978
[125]	validation_0-rmse:28970.3
[126]	validation_0-rmse:28980.8
[127]	validation_0-rmse:28975
[128]	validation_0-rmse:28985.7
[129]	validation_0-rmse:28977.6
[130]	validation_0-rmse:28913.9
[131]	validation_0-rmse:28911.1
[132]	validation_0-rmse:28899.4
[133]	validation_0-rmse:28920.1
[134]	validation_0-rmse:28925.3
[135]	validation_0-rmse:28943.1
[136]	validation_0-rmse:28940.2
[137]	validation_0-rmse:28950.3
[138]	validation_0-rmse:28990.8
[139]	validation_0-rmse:28985.3
[140]	validation_0-rmse:29013.2
[141]	validation_0-rmse:29006.1
[142]	validation_0-rmse:29014
[143]	validation_0-rmse:29032.2
[144]	validation_0-rmse:29015.3
[145]	validation_0-rmse:29015.4
[146]	validation_0-rmse:29031.8
[147]	validation_0-rmse:29023
[148]	validation_0-rmse:29024.2
[149]	validation_0-rmse:29044.8
[150]	validation_0-rmse:29035.8
[151]	validation_0-rmse:29046.7
[152]	validation_0-rmse:29022.3
[153]	validation_0-rmse:29024.6
[154]	validation_0-rmse:29021.1
[155]	validation_0-rmse:29028
[156]	validation_0-rmse:29021
[157]	validation_0-rmse:28965.1
[158]	validation_0-rmse:28969.9
[159]	validation_0-rmse:28930
[160]	validation_0-rmse:28924.3
[161]	validation_0-rmse:28929.7
[162]	validation_0-rmse:28931.4
[163]	validation_0-rmse:28925
[164]	validation_0-rmse:28924.5
[165]	validation_0-rmse:28921.3
[166]	validation_0-rmse:28946.2
[167]	validation_0-rmse:28943.3
[168]	validation_0-rmse:28940.3
[169]	validation_0-rmse:28947.2
[170]	validation_0-rmse:28935.8
[171]	validation_0-rmse:28938.7
[172]	validation_0-rmse:28932.8
[173]	validation_0-rmse:28934.6
[174]	validation_0-rmse:28928.2
[175]	validation_0-rmse:28928.9
[176]	validation_0-rmse:28902.6
[177]	validation_0-rmse:28912.8
[178]	validation_0-rmse:28920
[179]	validation_0-rmse:28922.6
[180]	validation_0-rmse:28921.1
[181]	validation_0-rmse:28936.4
[182]	validation_0-rmse:28928.9
[183]	validation_0-rmse:28933.2
[184]	validation_0-rmse:28943.1
[185]	validation_0-rmse:28962.6
[186]	validation_0-rmse:28954.3
[187]	validation_0-rmse:28951.6
[188]	validation_0-rmse:28974.4
[189]	validation_0-rmse:28970.6
[190]	validation_0-rmse:28963.1
[191]	validation_0-rmse:28939.3
[192]	validation_0-rmse:28924.6
[193]	validation_0-rmse:28928.5
[194]	validation_0-rmse:28901.6
[195]	validation_0-rmse:28909.4
[196]	validation_0-rmse:28916.1
[197]	validation_0-rmse:28901.4
[198]	validation_0-rmse:28938.1
[199]	validation_0-rmse:28940.1
[200]	validation_0-rmse:28932.4
[201]	validation_0-rmse:28934.9
[202]	validation_0-rmse:28936.3
[203]	validation_0-rmse:28922.4
[204]	validation_0-rmse:28926.8
[205]	validation_0-rmse:28932
[206]	validation_0-rmse:28939.5
[207]	validation_0-rmse:28921.5
[208]	validation_0-rmse:28919.8
[209]	validation_0-rmse:28924.3
[210]	validation_0-rmse:28925.9
[211]	validation_0-rmse:28919.2
[212]	validation_0-rmse:28917.9
[213]	validation_0-rmse:28917.9
[214]	validation_0-rmse:28912.3
[215]	validation_0-rmse:28898.2
[216]	validation_0-rmse:28912.9
[217]	validation_0-rmse:28916.5
[218]	validation_0-rmse:28914.3
[219]	validation_0-rmse:28911.6
[220]	validation_0-rmse:28895.6
[221]	validation_0-rmse:28901.2
[222]	validation_0-rmse:28905.6
[223]	validation_0-rmse:28926.4
[224]	validation_0-rmse:28944.4
[225]	validation_0-rmse:28926.4
[226]	validation_0-rmse:28925.8
[227]	validation_0-rmse:28930.3
[228]	validation_0-rmse:28955.5
[229]	validation_0-rmse:28927.8
[230]	validation_0-rmse:28928.9
[231]	validation_0-rmse:28921.9
[232]	validation_0-rmse:28915.9
[233]	validation_0-rmse:28908.6
[234]	validation_0-rmse:28903.8
[235]	validation_0-rmse:28896.9
[236]	validation_0-rmse:28888.9
[237]	validation_0-rmse:28880.6
[238]	validation_0-rmse:28879.3
[239]	validation_0-rmse:28883
[240]	validation_0-rmse:28876.1
[241]	validation_0-rmse:28869.3
[242]	validation_0-rmse:28859.6
[243]	validation_0-rmse:28858.3
[244]	validation_0-rmse:28852.6
[245]	validation_0-rmse:28853.7
[246]	validation_0-rmse:28840.7
[247]	validation_0-rmse:28847.1
[248]	validation_0-rmse:28857.7
[249]	validation_0-rmse:28852
[250]	validation_0-rmse:28854.6
[251]	validation_0-rmse:28820.1
[252]	validation_0-rmse:28805
[253]	validation_0-rmse:28828.3
[254]	validation_0-rmse:28833.4
[255]	validation_0-rmse:28827
[256]	validation_0-rmse:28824.3
[257]	validation_0-rmse:28828.1
[258]	validation_0-rmse:28825.4
[259]	validation_0-rmse:28820.9
[260]	validation_0-rmse:28820.4
[261]	validation_0-rmse:28822.9
[262]	validation_0-rmse:28825.8
[263]	validation_0-rmse:28825.9
[264]	validation_0-rmse:28835.6
[265]	validation_0-rmse:28834.3
[266]	validation_0-rmse:28829.8
[267]	validation_0-rmse:28814.6
[268]	validation_0-rmse:28819.8
[269]	validation_0-rmse:28823.9
[270]	validation_0-rmse:28819.2
[271]	validation_0-rmse:28826.2
[272]	validation_0-rmse:28853.1
[273]	validation_0-rmse:28859.1
[274]	validation_0-rmse:28866.2
[275]	validation_0-rmse:28866.5
[276]	validation_0-rmse:28867.7
[277]	validation_0-rmse:28870.8
[278]	validation_0-rmse:28876.8
[279]	validation_0-rmse:28870.4
[280]	validation_0-rmse:28887.9
[281]	validation_0-rmse:28894.4
[282]	validation_0-rmse:28908.6
[283]	validation_0-rmse:28893
[284]	validation_0-rmse:28901.3
[285]	validation_0-rmse:28904.4
[286]	validation_0-rmse:28913.4
[287]	validation_0-rmse:28907.5
[288]	validation_0-rmse:28914.9
[289]	validation_0-rmse:28915.4
[290]	validation_0-rmse:28908.2
[291]	validation_0-rmse:28903.2
[292]	validation_0-rmse:28901
[293]	validation_0-rmse:28898.2
[294]	validation_0-rmse:28898.6
[295]	validation_0-rmse:28909.1
[296]	validation_0-rmse:28914.4
[297]	validation_0-rmse:28925.6
[298]	validation_0-rmse:28924
[299]	validation_0-rmse:28920.3
[300]	validation_0-rmse:28926.2
[301]	validation_0-rmse:28929.9
[302]	validation_0-rmse:28928.4
[303]	validation_0-rmse:28933.1
[304]	validation_0-rmse:28920.7
[305]	validation_0-rmse:28915.5
[306]	validation_0-rmse:28922.2
[307]	validation_0-rmse:28917
[308]	validation_0-rmse:28918.5
[309]	validation_0-rmse:28918.4
[310]	validation_0-rmse:28917.9
[311]	validation_0-rmse:28926.3
[312]	validation_0-rmse:28892.9
[313]	validation_0-rmse:28885.2
[314]	validation_0-rmse:28890.8
[315]	validation_0-rmse:28894.7
[316]	validation_0-rmse:28881.6
[317]	validation_0-rmse:28858.4
[318]	validation_0-rmse:28860.7
[319]	validation_0-rmse:28857.3
[320]	validation_0-rmse:28858.5
[321]	validation_0-rmse:28858
[322]	validation_0-rmse:28847.7
[323]	validation_0-rmse:28851.3
[324]	validation_0-rmse:28856.9
[325]	validation_0-rmse:28866.9
[326]	validation_0-rmse:28865.9
[327]	validation_0-rmse:28869.5
[328]	validation_0-rmse:28871
[329]	validation_0-rmse:28874.1
[330]	validation_0-rmse:28863.4
[331]	validation_0-rmse:28854.3
[332]	validation_0-rmse:28857.5
[333]	validation_0-rmse:28854.1
[334]	validation_0-rmse:28851.4
[335]	validation_0-rmse:28852.2
[336]	validation_0-rmse:28877.6
[337]	validation_0-rmse:28872.9
[338]	validation_0-rmse:28876.8
[339]	validation_0-rmse:28877.4
[340]	validation_0-rmse:28884.2
[341]	validation_0-rmse:28886.1
[342]	validation_0-rmse:28885.9
[343]	validation_0-rmse:28887.1
[344]	validation_0-rmse:28894.4
[345]	validation_0-rmse:28894.3
[346]	validation_0-rmse:28896.1
[347]	validation_0-rmse:28893.6
[348]	validation_0-rmse:28893.9
[349]	validation_0-rmse:28883.1
[350]	validation_0-rmse:28893.8
[351]	validation_0-rmse:28889.1
[352]	validation_0-rmse:28897.9
[353]	validation_0-rmse:28896.1
[354]	validation_0-rmse:28894
[355]	validation_0-rmse:28890.4
[356]	validation_0-rmse:28899.3
[357]	validation_0-rmse:28896.8
[358]	validation_0-rmse:28906.3
[359]	validation_0-rmse:28908.2
[360]	validation_0-rmse:28902.9
[361]	validation_0-rmse:28896.6
[362]	validation_0-rmse:28902
[363]	validation_0-rmse:28897.8
[364]	validation_0-rmse:28908.5
[365]	validation_0-rmse:28908.7
[366]	validation_0-rmse:28909.6
[367]	validation_0-rmse:28917.4
[368]	validation_0-rmse:28918.3
[369]	validation_0-rmse:28912.8
[370]	validation_0-rmse:28907.3
[371]	validation_0-rmse:28903.3
[372]	validation_0-rmse:28901.6
[373]	validation_0-rmse:28899.8
[374]	validation_0-rmse:28901.6
[375]	validation_0-rmse:28894.2
[376]	validation_0-rmse:28894.8
[377]	validation_0-rmse:28888.4
[378]	validation_0-rmse:28881.4
[379]	validation_0-rmse:28894.2
[380]	validation_0-rmse:28893.6
[381]	validation_0-rmse:28879.3
[382]	validation_0-rmse:28877
[383]	validation_0-rmse:28879.1
[384]	validation_0-rmse:28892.3
[385]	validation_0-rmse:28900.9
[386]	validation_0-rmse:28877.5
[387]	validation_0-rmse:28882.3
[388]	validation_0-rmse:28895.5
[389]	validation_0-rmse:28897.4
[390]	validation_0-rmse:28907.4
[391]	validation_0-rmse:28910.5
[392]	validation_0-rmse:28911.6
[393]	validation_0-rmse:28911.6
[394]	validation_0-rmse:28915.3
[395]	validation_0-rmse:28914.9
[396]	validation_0-rmse:28910.1
[397]	validation_0-rmse:28910.6
[398]	validation_0-rmse:28913.2
[399]	validation_0-rmse:28913.3
[400]	validation_0-rmse:28913.4
[401]	validation_0-rmse:28911.5
[402]	validation_0-rmse:28910.4
[403]	validation_0-rmse:28916.3
[404]	validation_0-rmse:28908.8
[405]	validation_0-rmse:28905.1
[406]	validation_0-rmse:28904.4
[407]	validation_0-rmse:28913.4
[408]	validation_0-rmse:28908.5
[409]	validation_0-rmse:28905.5
[410]	validation_0-rmse:28899.5
[411]	validation_0-rmse:28900.5
[412]	validation_0-rmse:28902.3
[413]	validation_0-rmse:28900.6
[414]	validation_0-rmse:28891.7
[415]	validation_0-rmse:28890.1
[416]	validation_0-rmse:28890.3
[417]	validation_0-rmse:28891.3
[418]	validation_0-rmse:28886
[419]	validation_0-rmse:28879.5
[420]	validation_0-rmse:28874.5
[421]	validation_0-rmse:28888.2
[422]	validation_0-rmse:28892.8
[423]	validation_0-rmse:28891.8
[424]	validation_0-rmse:28890.3
[425]	validation_0-rmse:28890.7
[426]	validation_0-rmse:28890.3
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[428]	validation_0-rmse:28890
[429]	validation_0-rmse:28881.1
[430]	validation_0-rmse:28880
[431]	validation_0-rmse:28876.4
[432]	validation_0-rmse:28890.2
[433]	validation_0-rmse:28887.9
[434]	validation_0-rmse:28885.6
[435]	validation_0-rmse:28906.9
[436]	validation_0-rmse:28902.8
[437]	validation_0-rmse:28902.6
[438]	validation_0-rmse:28904.9
[439]	validation_0-rmse:28905.2
[440]	validation_0-rmse:28902.1
[441]	validation_0-rmse:28901.7
[442]	validation_0-rmse:28907
[443]	validation_0-rmse:28904.5
[444]	validation_0-rmse:28901.2
[445]	validation_0-rmse:28900.6
[446]	validation_0-rmse:28903.4
[447]	validation_0-rmse:28899.7
[448]	validation_0-rmse:28898.9
[449]	validation_0-rmse:28910.5
[450]	validation_0-rmse:28912.7
[451]	validation_0-rmse:28913.9
[452]	validation_0-rmse:28910.5
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[454]	validation_0-rmse:28923.3
[455]	validation_0-rmse:28929.6
[456]	validation_0-rmse:28925.7
[457]	validation_0-rmse:28923.8
[458]	validation_0-rmse:28929.6
[459]	validation_0-rmse:28925.1
[460]	validation_0-rmse:28930.4
[461]	validation_0-rmse:28927.4
[462]	validation_0-rmse:28917.7
[463]	validation_0-rmse:28909.1
[464]	validation_0-rmse:28907.8
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[467]	validation_0-rmse:28906.3
[468]	validation_0-rmse:28904
[469]	validation_0-rmse:28898.2
[470]	validation_0-rmse:28897.3
[471]	validation_0-rmse:28901.4
[472]	validation_0-rmse:28900.3
[473]	validation_0-rmse:28901.9
[474]	validation_0-rmse:28898.7
[475]	validation_0-rmse:28897.2
[476]	validation_0-rmse:28897.3
[477]	validation_0-rmse:28893.1
[478]	validation_0-rmse:28891.9
[479]	validation_0-rmse:28891.6
[480]	validation_0-rmse:28902.1
[481]	validation_0-rmse:28904.4
[482]	validation_0-rmse:28902
[483]	validation_0-rmse:28897.6
[484]	validation_0-rmse:28895.8
[485]	validation_0-rmse:28897.3
[486]	validation_0-rmse:28901.6
[487]	validation_0-rmse:28892.9
[488]	validation_0-rmse:28888.8
[489]	validation_0-rmse:28884.8
[490]	validation_0-rmse:28877.5
[491]	validation_0-rmse:28878.4
[492]	validation_0-rmse:28875.8
[493]	validation_0-rmse:28878.2
[494]	validation_0-rmse:28876.5
[495]	validation_0-rmse:28870.1
[496]	validation_0-rmse:28869.7
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[498]	validation_0-rmse:28868.4
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[500]	validation_0-rmse:28874
[501]	validation_0-rmse:28872.3
[502]	validation_0-rmse:28868.3
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[504]	validation_0-rmse:28870.8
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[506]	validation_0-rmse:28868.3
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[508]	validation_0-rmse:28872.7
[509]	validation_0-rmse:28870.1
[510]	validation_0-rmse:28869.5
[511]	validation_0-rmse:28871.1
[512]	validation_0-rmse:28863.8
[513]	validation_0-rmse:28865.1
[514]	validation_0-rmse:28860.9
[515]	validation_0-rmse:28857.9
[516]	validation_0-rmse:28855.7
[517]	validation_0-rmse:28853.2
[518]	validation_0-rmse:28855.5
[519]	validation_0-rmse:28861.7
[520]	validation_0-rmse:28861.7
[521]	validation_0-rmse:28859.4
[522]	validation_0-rmse:28860.4
[523]	validation_0-rmse:28860.2
[524]	validation_0-rmse:28857.7
[525]	validation_0-rmse:28857.9
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[527]	validation_0-rmse:28855.6
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[531]	validation_0-rmse:28860.3
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[533]	validation_0-rmse:28859.3
[534]	validation_0-rmse:28863.8
[535]	validation_0-rmse:28848.5
[536]	validation_0-rmse:28846.4
[537]	validation_0-rmse:28850.4
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[539]	validation_0-rmse:28847.7
[540]	validation_0-rmse:28846.1
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[544]	validation_0-rmse:28834.1
[545]	validation_0-rmse:28840.6
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[547]	validation_0-rmse:28845.4
[548]	validation_0-rmse:28838.7
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[550]	validation_0-rmse:28846.2
[551]	validation_0-rmse:28849.6
[552]	validation_0-rmse:28842.5
[553]	validation_0-rmse:28841.8
[554]	validation_0-rmse:28841.2
[555]	validation_0-rmse:28847.9
[556]	validation_0-rmse:28841.4
[557]	validation_0-rmse:28836.6
[558]	validation_0-rmse:28831.8
[559]	validation_0-rmse:28827.7
[560]	validation_0-rmse:28825.7
[561]	validation_0-rmse:28823.8
[562]	validation_0-rmse:28822.1
[563]	validation_0-rmse:28816.9
[564]	validation_0-rmse:28816.1
[565]	validation_0-rmse:28814.3
[566]	validation_0-rmse:28814.5
[567]	validation_0-rmse:28812.2
[568]	validation_0-rmse:28812.5
[569]	validation_0-rmse:28814.9
[570]	validation_0-rmse:28806.5
[571]	validation_0-rmse:28809.9
[572]	validation_0-rmse:28807.4
[573]	validation_0-rmse:28807.3
[574]	validation_0-rmse:28796.5
[575]	validation_0-rmse:28799.7
[576]	validation_0-rmse:28796
[577]	validation_0-rmse:28790.6
[578]	validation_0-rmse:28789.5
[579]	validation_0-rmse:28790.1
[580]	validation_0-rmse:28788.8
[581]	validation_0-rmse:28784.5
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[583]	validation_0-rmse:28778.9
[584]	validation_0-rmse:28777.6
[585]	validation_0-rmse:28774.6
[586]	validation_0-rmse:28773.2
[587]	validation_0-rmse:28776.1
[588]	validation_0-rmse:28775.8
[589]	validation_0-rmse:28780.4
[590]	validation_0-rmse:28783.6
[591]	validation_0-rmse:28785.2
[592]	validation_0-rmse:28778.1
[593]	validation_0-rmse:28775.9
[594]	validation_0-rmse:28774.9
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[596]	validation_0-rmse:28768.8
[597]	validation_0-rmse:28773.4
[598]	validation_0-rmse:28773.1
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[600]	validation_0-rmse:28775
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[603]	validation_0-rmse:28794.3
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[605]	validation_0-rmse:28790.4
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[608]	validation_0-rmse:28792.1
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[610]	validation_0-rmse:28790.3
[611]	validation_0-rmse:28796.5
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[613]	validation_0-rmse:28799.8
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[615]	validation_0-rmse:28800.9
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[621]	validation_0-rmse:28800.5
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[623]	validation_0-rmse:28802.3
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[625]	validation_0-rmse:28801.9
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[628]	validation_0-rmse:28795.4
[629]	validation_0-rmse:28790.9
[630]	validation_0-rmse:28791.1
[631]	validation_0-rmse:28791
[632]	validation_0-rmse:28782.3
[633]	validation_0-rmse:28780.3
[634]	validation_0-rmse:28779.4
[635]	validation_0-rmse:28780.9
[636]	validation_0-rmse:28777.3
[637]	validation_0-rmse:28773.7
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[646]	validation_0-rmse:28780.2
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[648]	validation_0-rmse:28778.8
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[651]	validation_0-rmse:28776.9
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[654]	validation_0-rmse:28777.4
[655]	validation_0-rmse:28781.1
[656]	validation_0-rmse:28776.8
[657]	validation_0-rmse:28775.4
[658]	validation_0-rmse:28771
[659]	validation_0-rmse:28770.4
[660]	validation_0-rmse:28778.1
[661]	validation_0-rmse:28769.4
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[663]	validation_0-rmse:28764.2
[664]	validation_0-rmse:28766.6
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[666]	validation_0-rmse:28760.7
[667]	validation_0-rmse:28763.4
[668]	validation_0-rmse:28758.1
[669]	validation_0-rmse:28760.9
[670]	validation_0-rmse:28761.8
[671]	validation_0-rmse:28759.2
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[674]	validation_0-rmse:28760.6
[675]	validation_0-rmse:28764.9
[676]	validation_0-rmse:28769.8
[677]	validation_0-rmse:28774.3
[678]	validation_0-rmse:28774
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[680]	validation_0-rmse:28774.2
[681]	validation_0-rmse:28774.6
[682]	validation_0-rmse:28764.7
[683]	validation_0-rmse:28764
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[685]	validation_0-rmse:28765.3
[686]	validation_0-rmse:28764.3
[687]	validation_0-rmse:28764.1
[688]	validation_0-rmse:28761.1
[689]	validation_0-rmse:28760.1
[690]	validation_0-rmse:28763.4
[691]	validation_0-rmse:28763.4
[692]	validation_0-rmse:28764
[693]	validation_0-rmse:28759.3
[694]	validation_0-rmse:28758.3
[695]	validation_0-rmse:28755.2
[696]	validation_0-rmse:28754.8
[697]	validation_0-rmse:28756.4
[698]	validation_0-rmse:28758.5
[699]	validation_0-rmse:28759.7





XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,
       colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0,
       max_depth=3, min_child_weight=1, missing=None, n_estimators=700,
       n_jobs=1, nthread=None, objective='reg:linear', random_state=0,
       reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,
       silent=True, subsample=1)

n_jobs: 大数据集上并行化

improved_model.score(X_test, y_test)

0.8453871314482886

#rmse
mean_absolute_error(improved_model.predict(X_test), y_test)

16569.6632844917

XGBoost一些参数

XGBRegressor/XGBClassifier(booster='gbtree', #gbtree树模型做基学习器（默认）  booster='gbliner'线性模型做基学习器
            n_estimators=700, #700个基学习器，迭代700轮（fit中展示训练情况，然后不断试）
            silent=True,         #不输出中间结果（默认）,沉默嘛
            learning_rate=0.1, #学习率（步长）  默认0.1 
            objective='reg:linear', #目标函数 
                                   #回归任务：
                                   #       reg:linear--默认
                                   #       reg:logistic
                                   #分类任务：
                                   #       binary:logistic--二分类  返回概率
                                   #       binary：logitraw-二分类  返回类别
                                   #       multi:softmax--多分类  使用softmax的多分类器，返回预测的类别。需要多设一个参数：num_class(类别数目)
                                   #       multi:softprob--多分类  返回的是每个数据属于各个类别的概率。需要多设一个参数：num_class(类别数目)
            max_depth=3, 
            min_child_weight=1,    #???????????????没有太理解
            n_jobs=1,             
            random_state=0,
            scale_pos_weight=1    #正positive样本权重当正负样本比例失衡时，设置正样本的权重
            reg_alpha=0,              #L1正则化系数
            reg_lambda=1,             #L2正则化系数
            subsample=1,          #随机采样的样本比例
            silent=True)          ##不输出中间结果（默认）,沉默嘛

method fit in module xgboost.sklearn:
help(improved_model.fit)
improved_mode.fit( X, #feature matrix  array_like
                  y,  #lables          array_like
                  sample_weight, #样本权重  array_like
                  eval_set,      #验证集  list,可选，训练集拟合，用验证集估计误差，若验证集误差升高，则early-stopping
                  sample_weight_eval_set,
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                                  #回归任务：rmse--均方根误差（默认）
                                  #           mae--平均绝对误差
                                  #分类任务：error--错误率（二分类）（默认）
                                  #          auc-roc曲线下面积
                                  #          merror--错误率（多分类）
                                  #          logloss--负对数似然函数（二分类）
                                  #          mlogloss--负对数似然函数（多分类）
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                  verbose,       # bool  为True，则输出每轮的evaluation metric情况  bool
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