Kaggle competition: Quora Insincere Questions Classification
-1*random_search_lgb_model.best_score_<find_best_params>
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random_search_lgb_model.best_estimator_<choose_model_class>
qids_18= ['b7b62ee04de1a2f61679',
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lgb_model_tuned = lgb.LGBMRegressor(boosting='gbdt', boosting_type='gbdt', class_weight=None,
colsample_bytree=1.0, importance_type='split', learning_rate=0.05,
max_depth=-1, min_child_samples=20, min_child_weight=0.001,
min_split_gain=0.0, n_estimators=1000, n_jobs=-1, num_leaves=31,
objective=None, random_state=None, reg_alpha=0.0, reg_lambda=0.0,
silent=True, subsample=1.0, subsample_for_bin=200000,
subsample_freq=0)
 lgb_model_tuned.fit(valid_data, y_valid )<compute_test_metric>
qids_19 = ['c2542370907e65b45b9d',
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lgb_model_tuned.score(valid_data, y_valid )<define_search_space>
qids_20=['cc8fe31d6c57f66e1c23',
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params_for_rf_model = {
'n_estimators': [500, 1000, 1500, 2000]
}<choose_model_class>
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'e1def5bec8418a4e1064',
'e1e053b852ee6cec5a50',
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random_search_rf_model = RandomizedSearchCV(
 estimator=rf_model,
param_distributions=params_for_rf_model,
scoring='neg_mean_absolute_error',
n_iter=5,
n_jobs=-1,
cv=3,
verbose=True
)<train_model>
qids_22=['e226b2fd842549fd06ea',
'e228a98c35721a1b2c68',
'e22fc78064d59be88bce',
'e23582680b55ee28b737',
'e2382b29f09292bf8bec',
'e23b1036ae86bb620bac',
'e23b1c4df79db7fb2b9b',
'e23e469eebe218710011',
'e24084cb613ee074812d',
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random_search_rf_model.fit(train_data, y_train )<find_best_params>
qids_23=['ed0f5064f3c67b2df91f',
'ed11ba2c41c26927f9d9',
'ed165d431802c6d74f3c',
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-1*random_search_rf_model.best_score_<find_best_params>
qids_24=['f8694f97583bf9ab2412',
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random_search_rf_model.best_estimator_<choose_model_class>
qids_all = qids_1+qids_2+qids_3+qids_4+qids_5+qids_6+qids_7+qids_8+qids_9+qids_10+qids_11+qids_12+qids_13+qids_14+qids_15+qids_16+qids_17+qids_18+qids_19+qids_20+qids_21+qids_22+qids_23+qids_24
rf_model_tuned = RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',
max_depth=None, max_features='auto', max_leaf_nodes=None,
max_samples=None, min_impurity_decrease=0.0,
min_impurity_split=None, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=2000, n_jobs=None, oob_score=False,
random_state=None, verbose=0, warm_start=False)
 rf_model_tuned.fit(train_data, y_train )<compute_test_metric>
qid = []
with open('/kaggle/input/quora-insincere-questions-classification/test.csv', 'r')as read_obj,open('result.csv', 'w', newline='')as write_obj:
reader = csv.reader(read_obj)
 writer = csv.writer(write_obj, lineterminator='
')
 all_lines = []
row = []
row.append('qid')
 row.append('prediction')
 all_lines.append(row)
 line = next(reader)
 for rr in reader:
new_row = []
new_row.append(rr[0])
 if rr[0] in qids_all:
new_row.append(1)
 else:
new_row.append(0)
 all_lines.append(new_row)
 writer.writerows(all_lines )
rf_model_tuned.score(valid_data, y_valid )<define_search_space>
sub = pd.read_csv('/kaggle/working/result.csv')
 sub.to_csv('submission.csv',index=False)