XGBoost models

XGBoost Article

The data here is taken form the Data Hackathon3.x - http://datahack.analyticsvidhya.com/contest/data-hackathon-3x

 

Import Libraries:

In [1]:

import pandas as pd
import numpy as np import xgboost as xgb from xgboost.sklearn import XGBClassifier from sklearn import cross_validation, metrics from sklearn.grid_search import GridSearchCV import matplotlib.pylab as plt %matplotlib inline from matplotlib.pylab import rcParams rcParams['figure.figsize'] = 12, 4 

 

Load Data:

The data has gone through following pre-processing:

1. City variable dropped because of too many categories
2. DOB converted to Age | DOB dropped
3. EMI_Loan_Submitted_Missing created which is 1 if EMI_Loan_Submitted was missing else 0 | EMI_Loan_Submitted dropped
4. EmployerName dropped because of too many categories
5. Existing_EMI imputed with 0 (median) - 111 values were missing
6. Interest_Rate_Missing created which is 1 if Interest_Rate was missing else 0 | Interest_Rate dropped
7. Lead_Creation_Date dropped because made little intuitive impact on outcome
8. Loan_Amount_Applied, Loan_Tenure_Applied imputed with missing
9. Loan_Amount_Submitted_Missing created which is 1 if Loan_Amount_Submitted was missing else 0 | Loan_Amount_Submitted dropped
10. Loan_Tenure_Submitted_Missing created which is 1 if Loan_Tenure_Submitted was missing else 0 | Loan_Tenure_Submitted dropped
11. LoggedIn, Salary_Account removed
12. Processing_Fee_Missing created which is 1 if Processing_Fee was missing else 0 | Processing_Fee dropped
13. Source - top 2 kept as is and all others combined into different category
14. Numerical and One-Hot-Coding performed

In [2]:

train = pd.read_csv('train_modified.csv') test = pd.read_csv('test_modified.csv') 

In [3]:

train.shape, test.shape 

Out[3]:

((87020, 51), (37717, 50))

In [4]:

target='Disbursed'
IDcol = 'ID' 

In [5]:

train['Disbursed'].value_counts() 

Out[5]:

0    85747
1     1273
Name: Disbursed, dtype: int64

 

Define a function for modeling and cross-validation

This function will do the following:

1. fit the model
2. determine training accuracy
3. determine training AUC
4. determine testing AUC
5. update n_estimators with cv function of xgboost package
6. plot Feature Importance

In [6]:

test_results = pd.read_csv('test_results.csv') def modelfit(alg, dtrain, dtest, predictors,useTrainCV=True, cv_folds=5, early_stopping_rounds=50): if useTrainCV: xgb_param = alg.get_xgb_params() xgtrain = xgb.DMatrix(dtrain[predictors].values, label=dtrain[target].values) xgtest = xgb.DMatrix(dtest[predictors].values) cvresult = xgb.cv(xgb_param, xgtrain, num_boost_round=alg.get_params()['n_estimators'], nfold=cv_folds, metrics='auc', early_stopping_rounds=early_stopping_rounds, show_progress=False) alg.set_params(n_estimators=cvresult.shape[0]) #Fit the algorithm on the data alg.fit(dtrain[predictors], dtrain['Disbursed'],eval_metric='auc') #Predict training set: dtrain_predictions = alg.predict(dtrain[predictors]) dtrain_predprob = alg.predict_proba(dtrain[predictors])[:,1] #Print model report: print "\nModel Report" print "Accuracy : %.4g" % metrics.accuracy_score(dtrain['Disbursed'].values, dtrain_predictions) print "AUC Score (Train): %f" % metrics.roc_auc_score(dtrain['Disbursed'], dtrain_predprob) # Predict on testing data: dtest['predprob'] = alg.predict_proba(dtest[predictors])[:,1] results = test_results.merge(dtest[['ID','predprob']], on='ID') print 'AUC Score (Test): %f' % metrics.roc_auc_score(results['Disbursed'], results['predprob']) feat_imp = pd.Series(alg.booster().get_fscore()).sort_values(ascending=False) feat_imp.plot(kind='bar', title='Feature Importances') plt.ylabel('Feature Importance Score') 

 

Step 1- Find the number of estimators for a high learning rate

In [8]:

predictors = [x for x in train.columns if x not in [target, IDcol]] xgb1 = XGBClassifier( learning_rate =0.1, n_estimators=1000, max_depth=5, min_child_weight=1, gamma=0, subsample=0.8, colsample_bytree=0.8, objective= 'binary:logistic', nthread=4, scale_pos_weight=1, seed=27) modelfit(xgb1, train, test, predictors) 

 

Will train until cv error hasn't decreased in 50 rounds.
Stopping. Best iteration:
[140] cv-mean:0.843638	cv-std:0.0141274405467

 

Model Report
Accuracy : 0.9854
AUC Score (Train): 0.899857
AUC Score (Test): 0.847934

 

In [9]:

#Grid seach on subsample and max_features
#Choose all predictors except target & IDcols
param_test1 = {
    'max_depth':range(3,10,2), 'min_child_weight':range(1,6,2) } gsearch1 = GridSearchCV(estimator = XGBClassifier( learning_rate =0.1, n_estimators=140, max_depth=5, min_child_weight=1, gamma=0, subsample=0.8, colsample_bytree=0.8, objective= 'binary:logistic', nthread=4, scale_pos_weight=1, seed=27), param_grid = param_test1, scoring='roc_auc',n_jobs=4,iid=False, cv=5) gsearch1.fit(train[predictors],train[target]) 

Out[9]:

GridSearchCV(cv=5, error_score='raise',
       estimator=XGBClassifier(base_score=0.5, colsample_bylevel=1, colsample_bytree=0.8,
       gamma=0, learning_rate=0.1, max_delta_step=0, max_depth=5,
       min_child_weight=1, missing=None, n_estimators=140, nthread=4,
       objective='binary:logistic', reg_alpha=0, reg_lambda=1,
       scale_pos_weight=1, seed=27, silent=True, subsample=0.8),
       fit_params={}, iid=False, n_jobs=4,
       param_grid={'max_depth': [3, 5, 7, 9], 'min_child_weight': [1, 3, 5]},
       pre_dispatch='2*n_jobs', refit=True, scoring='roc_auc', verbose=0)

In [10]:

gsearch1.grid_scores_, gsearch1.best_params_, gsearch1.best_score_ 

Out[10]:

([mean: 0.83690, std: 0.00821, params: {'max_depth': 3, 'min_child_weight': 1},
  mean: 0.83730, std: 0.00858, params: {'max_depth': 3, 'min_child_weight': 3},
  mean: 0.83713, std: 0.00847, params: {'max_depth': 3, 'min_child_weight': 5},
  mean: 0.84051, std: 0.00748, params: {'max_depth': 5, 'min_child_weight': 1},
  mean: 0.84112, std: 0.00595, params: {'max_depth': 5, 'min_child_weight': 3},
  mean: 0.84123, std: 0.00619, params: {'max_depth': 5, 'min_child_weight': 5},
  mean: 0.83772, std: 0.00518, params: {'max_depth': 7, 'min_child_weight': 1},
  mean: 0.83672, std: 0.00579, params: {'max_depth': 7, 'min_child_weight': 3},
  mean: 0.83658, std: 0.00355, params: {'max_depth': 7, 'min_child_weight': 5},
  mean: 0.82690, std: 0.00622, params: {'max_depth': 9, 'min_child_weight': 1},
  mean: 0.82909, std: 0.00560, params: {'max_depth': 9, 'min_child_weight': 3},
  mean: 0.83211, std: 0.00707, params: {'max_depth': 9, 'min_child_weight': 5}],
 {'max_depth': 5, 'min_child_weight': 5},
 0.84123292820257589)

In [11]:

#Grid seach on subsample and max_features
#Choose all predictors except target & IDcols
param_test2 = {
    'max_depth':[4,5,6], 'min_child_weight':[4,5,6] } gsearch2 = GridSearchCV(estimator = XGBClassifier( learning_rate=0.1, n_estimators=140, max_depth=5, min_child_weight=2, gamma=0, subsample=0.8, colsample_bytree=0.8, objective= 'binary:logistic', nthread=4, scale_pos_weight=1,seed=27), param_grid = param_test2, scoring='roc_auc',n_jobs=4,iid=False, cv=5) gsearch2.fit(train[predictors],train[target]) 

Out[11]:

GridSearchCV(cv=5, error_score='raise',
       estimator=XGBClassifier(base_score=0.5, colsample_bylevel=1, colsample_bytree=0.8,
       gamma=0, learning_rate=0.1, max_delta_step=0, max_depth=5,
       min_child_weight=2, missing=None, n_estimators=140, nthread=4,
       objective='binary:logistic', reg_alpha=0, reg_lambda=1,
       scale_pos_weight=1, seed=27, silent=True, subsample=0.8),
       fit_params={}, iid=False, n_jobs=4,
       param_grid={'max_depth': [4, 5, 6], 'min_child_weight': [4, 5, 6]},
       pre_dispatch='2*n_jobs', refit=True, scoring='roc_auc', verbose=0)

In [12]:

gsearch2.grid_scores_, gsearch2.best_params_, gsearch2.best_score_ 

Out[12]:

([mean: 0.84031, std: 0.00658, params: {'max_depth': 4, 'min_child_weight': 4},
  mean: 0.84061, std: 0.00700, params: {'max_depth': 4, 'min_child_weight': 5},
  mean: 0.84125, std: 0.00723, params: {'max_depth': 4, 'min_child_weight': 6},
  mean: 0.83988, std: 0.00612, params: {'max_depth': 5, 'min_child_weight': 4},
  mean: 0.84123, std: 0.00619, params: {'max_depth': 5, 'min_child_weight': 5},
  mean: 0.83995, std: 0.00591, params: {'max_depth': 5, 'min_child_weight': 6},
  mean: 0.83905, std: 0.00635, params: {'max_depth': 6, 'min_child_weight': 4},
  mean: 0.83904, std: 0.00656, params: {'max_depth': 6, 'min_child_weight': 5},
  mean: 0.83844, std: 0.00682, params: {'max_depth': 6, 'min_child_weight': 6}],
 {'max_depth': 4, 'min_child_weight': 6},
 0.84124915179964577)

In [13]:

#Grid seach on subsample and max_features
#Choose all predictors except target & IDcols
param_test2b = {
    'min_child_weight':[6,8,10,12] } gsearch2b = GridSearchCV(estimator = XGBClassifier( learning_rate=0.1, n_estimators=140, max_depth=4, min_child_weight=2, gamma=0, subsample=0.8, colsample_bytree=0.8, objective= 'binary:logistic', nthread=4, scale_pos_weight=1,seed=27), param_grid = param_test2b, scoring='roc_auc',n_jobs=4,iid=False, cv=5) gsearch2b.fit(train[predictors],train[target]) 

Out[13]:

GridSearchCV(cv=5, error_score='raise',
       estimator=XGBClassifier(base_score=0.5, colsample_bylevel=1, colsample_bytree=0.8,
       gamma=0, learning_rate=0.1, max_delta_step=0, max_depth=4,
       min_child_weight=2, missing=None, n_estimators=140, nthread=4,
       objective='binary:logistic', reg_alpha=0, reg_lambda=1,
       scale_pos_weight=1, seed=27, silent=True, subsample=0.8),
       fit_params={}, iid=False, n_jobs=4,
       param_grid={'min_child_weight': [6, 8, 10, 12]},
       pre_dispatch='2*n_jobs', refit=True, scoring='roc_auc', verbose=0)

In [14]:

gsearch2b.grid_scores_, gsearch2b.best_params_, gsearch2b.best_score_ 

Out[14]:

([mean: 0.84125, std: 0.00723, params: {'min_child_weight': 6},
  mean: 0.84028, std: 0.00710, params: {'min_child_weight': 8},
  mean: 0.83920, std: 0.00674, params: {'min_child_weight': 10},
  mean: 0.83996, std: 0.00729, params: {'min_child_weight': 12}],
 {'min_child_weight': 6},
 0.84124915179964577)

In [17]:

#Grid seach on subsample and max_features
#Choose all predictors except target & IDcols
param_test3 = {
    'gamma':[i/10.0 for i in range(0,5)] } gsearch3 = GridSearchCV(estimator = XGBClassifier( learning_rate =0.1, n_estimators=140, max_depth=4, min_child_weight=6, gamma=0, subsample=0.8, colsample_bytree=0.8, objective= 'binary:logistic', nthread=4, scale_pos_weight=1,seed=27), param_grid = param_test3, scoring='roc_auc',n_jobs=4,iid=False, cv=5) gsearch3.fit(train[predictors],train[target]) 

Out[17]:

GridSearchCV(cv=5, error_score='raise',
       estimator=XGBClassifier(base_score=0.5, colsample_bylevel=1, colsample_bytree=0.8,
       gamma=0, learning_rate=0.1, max_delta_step=0, max_depth=4,
       min_child_weight=6, missing=None, n_estimators=140, nthread=4,
       objective='binary:logistic', reg_alpha=0, reg_lambda=1,
       scale_pos_weight=1, seed=27, silent=True, subsample=0.8),
       fit_params={}, iid=False, n_jobs=4,
       param_grid={'gamma': [0.0, 0.1, 0.2, 0.3, 0.4]},
       pre_dispatch='2*n_jobs', refit=True, scoring='roc_auc', verbose=0)

In [18]:

gsearch3.grid_scores_, gsearch3.best_params_, gsearch3.best_score_ 

Out[18]:

([mean: 0.84125, std: 0.00723, params: {'gamma': 0.0},
  mean: 0.83996, std: 0.00695, params: {'gamma': 0.1},
  mean: 0.84045, std: 0.00639, params: {'gamma': 0.2},
  mean: 0.84032, std: 0.00673, params: {'gamma': 0.3},
  mean: 0.84061, std: 0.00692, params: {'gamma': 0.4}],
 {'gamma': 0.0},
 0.84124915179964577)

In [19]:

predictors = [x for x in train.columns if x not in [target, IDcol]] xgb2 = XGBClassifier( learning_rate =0.1, n_estimators=1000, max_depth=4, min_child_weight=6, gamma=0, subsample=0.8, colsample_bytree=0.8, objective= 'binary:logistic', nthread=4, scale_pos_weight=1, seed=27) modelfit(xgb2, train, test, predictors) 

 

Will train until cv error hasn't decreased in 50 rounds.
Stopping. Best iteration:
[177] cv-mean:0.8451166	cv-std:0.0123406045006

 

Model Report
Accuracy : 0.9854
AUC Score (Train): 0.883836
AUC Score (Test): 0.848967

 

 

Tune subsample and colsample_bytree

In [20]:

#Grid seach on subsample and max_features
#Choose all predictors except target & IDcols
param_test4 = {
    'subsample':[i/10.0 for i in range(6,10)], 'colsample_bytree':[i/10.0 for i in range(6,10)] } gsearch4 = GridSearchCV(estimator = XGBClassifier( learning_rate =0.1, n_estimators=177, max_depth=4, min_child_weight=6, gamma=0, subsample=0.8, colsample_bytree=0.8, objective= 'binary:logistic', nthread=4, scale_pos_weight=1,seed=27), param_grid = param_test4, scoring='roc_auc',n_jobs=4,iid=False, cv=5) gsearch4.fit(train[predictors],train[target]) 

Out[20]:

GridSearchCV(cv=5, error_score='raise',
       estimator=XGBClassifier(base_score=0.5, colsample_bylevel=1, colsample_bytree=0.8,
       gamma=0, learning_rate=0.1, max_delta_step=0, max_depth=4,
       min_child_weight=6, missing=None, n_estimators=177, nthread=4,
       objective='binary:logistic', reg_alpha=0, reg_lambda=1,
       scale_pos_weight=1, seed=27, silent=True, subsample=0.8),
       fit_params={}, iid=False, n_jobs=4,
       param_grid={'subsample': [0.6, 0.7, 0.8, 0.9], 'colsample_bytree': [0.6, 0.7, 0.8, 0.9]},
       pre_dispatch='2*n_jobs', refit=True, scoring='roc_auc', verbose=0)

In [21]:

gsearch4.grid_scores_, gsearch4.best_params_, gsearch4.best_score_ 

Out[21]:

([mean: 0.83688, std: 0.00849, params: {'subsample': 0.6, 'colsample_bytree': 0.6},
  mean: 0.83834, std: 0.00772, params: {'subsample': 0.7, 'colsample_bytree': 0.6},
  mean: 0.83946, std: 0.00813, params: {'subsample': 0.8, 'colsample_bytree': 0.6},
  mean: 0.83845, std: 0.00831, params: {'subsample': 0.9, 'colsample_bytree': 0.6},
  mean: 0.83816, std: 0.00651, params: {'subsample': 0.6, 'colsample_bytree': 0.7},
  mean: 0.83797, std: 0.00668, params: {'subsample': 0.7, 'colsample_bytree': 0.7},
  mean: 0.83956, std: 0.00824, params: {'subsample': 0.8, 'colsample_bytree': 0.7},
  mean: 0.83892, std: 0.00626, params: {'subsample': 0.9, 'colsample_bytree': 0.7},
  mean: 0.83914, std: 0.00794, params: {'subsample': 0.6, 'colsample_bytree': 0.8},
  mean: 0.83974, std: 0.00687, params: {'subsample': 0.7, 'colsample_bytree': 0.8},
  mean: 0.84102, std: 0.00715, params: {'subsample': 0.8, 'colsample_bytree': 0.8},
  mean: 0.84029, std: 0.00645, params: {'subsample': 0.9, 'colsample_bytree': 0.8},
  mean: 0.83881, std: 0.00723, params: {'subsample': 0.6, 'colsample_bytree': 0.9},
  mean: 0.83975, std: 0.00706, params: {'subsample': 0.7, 'colsample_bytree': 0.9},
  mean: 0.83975, std: 0.00648, params: {'subsample': 0.8, 'colsample_bytree': 0.9},
  mean: 0.83954, std: 0.00698, params: {'subsample': 0.9, 'colsample_bytree': 0.9}],
 {'colsample_bytree': 0.8, 'subsample': 0.8},
 0.8410246925643593)

 

tune subsample:

In [22]:

#Grid seach on subsample and max_features
#Choose all predictors except target & IDcols
param_test5 = {
    'subsample':[i/100.0 for i in range(75,90,5)], 'colsample_bytree':[i/100.0 for i in range(75,90,5)] } gsearch5 = GridSearchCV(estimator = XGBClassifier( learning_rate =0.1, n_estimators=177, max_depth=4, min_child_weight=6, gamma=0, subsample=0.8, colsample_bytree=0.8, objective= 'binary:logistic', nthread=4, scale_pos_weight=1,seed=27), param_grid = param_test5, scoring='roc_auc',n_jobs=4,iid=False, cv=5) gsearch5.fit(train[predictors],train[target]) 

Out[22]:

GridSearchCV(cv=5, error_score='raise',
       estimator=XGBClassifier(base_score=0.5, colsample_bylevel=1, colsample_bytree=0.8,
       gamma=0, learning_rate=0.1, max_delta_step=0, max_depth=4,
       min_child_weight=6, missing=None, n_estimators=177, nthread=4,
       objective='binary:logistic', reg_alpha=0, reg_lambda=1,
       scale_pos_weight=1, seed=27, silent=True, subsample=0.8),
       fit_params={}, iid=False, n_jobs=4,
       param_grid={'subsample': [0.75, 0.8, 0.85], 'colsample_bytree': [0.75, 0.8, 0.85]},
       pre_dispatch='2*n_jobs', refit=True, scoring='roc_auc', verbose=0)

In [25]:

gsearch5.grid_scores_, gsearch5.best_params_, gsearch5.best_score_ 

Out[25]:

([mean: 0.83881, std: 0.00795, params: {'subsample': 0.75, 'colsample_bytree': 0.75},
  mean: 0.84037, std: 0.00638, params: {'subsample': 0.8, 'colsample_bytree': 0.75},
  mean: 0.84013, std: 0.00685, params: {'subsample': 0.85, 'colsample_bytree': 0.75},
  mean: 0.83967, std: 0.00694, params: {'subsample': 0.75, 'colsample_bytree': 0.8},
  mean: 0.84102, std: 0.00715, params: {'subsample': 0.8, 'colsample_bytree': 0.8},
  mean: 0.84087, std: 0.00693, params: {'subsample': 0.85, 'colsample_bytree': 0.8},
  mean: 0.83836, std: 0.00738, params: {'subsample': 0.75, 'colsample_bytree': 0.85},
  mean: 0.84067, std: 0.00698, params: {'subsample': 0.8, 'colsample_bytree': 0.85},
  mean: 0.83978, std: 0.00689, params: {'subsample': 0.85, 'colsample_bytree': 0.85}],
 {'colsample_bytree': 0.8, 'subsample': 0.8},
 0.8410246925643593)

 

Got the same value as assument and no change requried.

 

Try regularization:

In [24]:

#Grid seach on subsample and max_features
#Choose all predictors except target & IDcols
param_test6 = {
    'reg_alpha':[1e-5, 1e-2, 0.1, 1, 100] } gsearch6 = GridSearchCV(estimator = XGBClassifier( learning_rate =0.1, n_estimators=177, max_depth=4, min_child_weight=6, gamma=0.1, subsample=0.8, colsample_bytree=0.8, objective= 'binary:logistic', nthread=4, scale_pos_weight=1,seed=27), param_grid = param_test6, scoring='roc_auc',n_jobs=4,iid=False, cv=5) gsearch6.fit(train[predictors],train[target]) 

Out[24]:

GridSearchCV(cv=5, error_score='raise',
       estimator=XGBClassifier(base_score=0.5, colsample_bylevel=1, colsample_bytree=0.8,
       gamma=0.1, learning_rate=0.1, max_delta_step=0, max_depth=4,
       min_child_weight=6, missing=None, n_estimators=177, nthread=4,
       objective='binary:logistic', reg_alpha=0, reg_lambda=1,
       scale_pos_weight=1, seed=27, silent=True, subsample=0.8),
       fit_params={}, iid=False, n_jobs=4,
       param_grid={'reg_alpha': [1e-05, 0.01, 0.1, 1, 100]},
       pre_dispatch='2*n_jobs', refit=True, scoring='roc_auc', verbose=0)

In [26]:

gsearch6.grid_scores_, gsearch6.best_params_, gsearch6.best_score_ 

Out[26]:

([mean: 0.83999, std: 0.00643, params: {'reg_alpha': 1e-05},
  mean: 0.84084, std: 0.00639, params: {'reg_alpha': 0.01},
  mean: 0.83985, std: 0.00831, params: {'reg_alpha': 0.1},
  mean: 0.83989, std: 0.00707, params: {'reg_alpha': 1},
  mean: 0.81343, std: 0.01541, params: {'reg_alpha': 100}],
 {'reg_alpha': 0.01},
 0.84084269674772316)

In [27]:

#Grid seach on subsample and max_features
#Choose all predictors except target & IDcols
param_test7 = {
    'reg_alpha':[0, 0.001, 0.005, 0.01, 0.05] } gsearch7 = GridSearchCV(estimator = XGBClassifier( learning_rate =0.1, n_estimators=177, max_depth=4, min_child_weight=6, gamma=0.1, subsample=0.8, colsample_bytree=0.8, objective= 'binary:logistic', nthread=4, scale_pos_weight=1,seed=27), param_grid = param_test7, scoring='roc_auc',n_jobs=4,iid=False, cv=5) gsearch7.fit(train[predictors],train[target]) 

Out[27]:

GridSearchCV(cv=5, error_score='raise',
       estimator=XGBClassifier(base_score=0.5, colsample_bylevel=1, colsample_bytree=0.8,
       gamma=0.1, learning_rate=0.1, max_delta_step=0, max_depth=4,
       min_child_weight=6, missing=None, n_estimators=177, nthread=4,
       objective='binary:logistic', reg_alpha=0, reg_lambda=1,
       scale_pos_weight=1, seed=27, silent=True, subsample=0.8),
       fit_params={}, iid=False, n_jobs=4,
       param_grid={'reg_alpha': [0, 0.001, 0.005, 0.01, 0.05]},
       pre_dispatch='2*n_jobs', refit=True, scoring='roc_auc', verbose=0)

In [28]:

gsearch7.grid_scores_, gsearch7.best_params_, gsearch7.best_score_ 

Out[28]:

([mean: 0.83999, std: 0.00643, params: {'reg_alpha': 0},
  mean: 0.83978, std: 0.00663, params: {'reg_alpha': 0.001},
  mean: 0.84118, std: 0.00651, params: {'reg_alpha': 0.005},
  mean: 0.84084, std: 0.00639, params: {'reg_alpha': 0.01},
  mean: 0.84008, std: 0.00690, params: {'reg_alpha': 0.05}],
 {'reg_alpha': 0.005},
 0.84118352535245489)

In [29]:

xgb3 = XGBClassifier(
        learning_rate =0.1, n_estimators=1000, max_depth=4, min_child_weight=6, gamma=0, subsample=0.8, colsample_bytree=0.8, reg_alpha=0.005, objective= 'binary:logistic', nthread=4, scale_pos_weight=1, seed=27) modelfit(xgb3, train, test, predictors) 

 

Will train until cv error hasn't decreased in 50 rounds.
Stopping. Best iteration:
[188] cv-mean:0.844475	cv-std:0.0129019770268

 

Model Report
Accuracy : 0.9854
AUC Score (Train): 0.887149
AUC Score (Test): 0.848972

 

In [30]:

xgb4 = XGBClassifier(
        learning_rate =0.01, n_estimators=5000, max_depth=4, min_child_weight=6, gamma=0, subsample=0.8, colsample_bytree=0.8, reg_alpha=0.005, objective= 'binary:logistic', nthread=4, scale_pos_weight=1, seed=27) modelfit(xgb4, train, test, predictors) 

 

Will train until cv error hasn't decreased in 50 rounds.
Stopping. Best iteration:
[1732] cv-mean:0.8452782	cv-std:0.0126670016879

 

Model Report
Accuracy : 0.9854
AUC Score (Train): 0.885261
AUC Score (Test): 0.849430