Kaggle competition: Home Credit Default Risk
<SOS> metric: AUC Kaggle data source: home-credit-default-risk<define_search_space>
import numpy as np
import pandas as pd
import os
np.argsort([1,7,4,9] )<sort_values>
app_train = pd.read_csv('.. /input/home-credit-simple-featuers/simple_features_train.csv')
 app_test = pd.read_csv('.. /input/home-credit-simple-featuers/simple_features_test.csv' )
distanz[np.argsort(distanz)[:k]]
np.min(distanz )<sort_values>
sk_id = pd.read_csv('.. /input/home-credit-default-risk/application_test.csv' )
ytrain[np.argsort(distanz)[:k]]<define_search_space>
train = app_train.drop(columns = ['TARGET'])
 train_labels = app_train['TARGET']
[1,0,1,0,1,1,0]<define_search_space>
X_train, X_test, y_train, y_test = train_test_split(
 train, train_labels, test_size=0.2 )
np.around(np.mean(np.array([1,0,1,0,1,1,0])) )<define_search_space>
clf = LGBMClassifier(boosting_type = 'goss', n_estimators = 10000, learning_rate= 0.005134, num_leaves= 54, max_depth= 10, subsample_for_bin= 240000, reg_alpha= 0.436193, reg_lambda= 0.479169, colsample_bytree=0.508716, min_split_gain= 0.024766, subsample= 1, is_unbalance= False, silent=-1, verbose=-1)
 clf.fit(X_train, y_train, eval_set=[(X_train, y_train),(X_test, y_test)],
eval_metric= 'auc', verbose= 100, early_stopping_rounds= 200)
np.median([1,0,1,0,1,1,0] )<sort_values>
<sort_values><EOS> 
y_pred = clf.predict_proba(app_test, num_iteration=clf.best_iteration_)[:, 1]
submit = sk_id[['SK_ID_CURR']]
submit['TARGET'] = y_pred
submit.to_csv('sub1.csv', index = False)