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Use ROC-AUC metric for classification model in examples #5440

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merged 5 commits into from
Aug 30, 2022
Merged

Use ROC-AUC metric for classification model in examples #5440

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90b16fb
[Python-package] FIX fix the metrics of classify module
Green-16 Aug 25, 2022
daa7dd4
[Python-package] FIX fix the metrics of classify module
Green-16 Aug 25, 2022
ddb03a8
[Python-package] FIX fix the metrics of classify module
Green-16 Aug 26, 2022
3074c8d
[Python-package] FIX fix the metrics of classify module
Green-16 Aug 29, 2022
0e824d8
[Python-package] FIX fix the metrics of classify module
Green-16 Aug 29, 2022
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10 changes: 5 additions & 5 deletions examples/python-guide/advanced_example.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,7 +6,7 @@

import numpy as np
import pandas as pd
from sklearn.metrics import mean_squared_error
from sklearn.metrics import roc_auc_score

import lightgbm as lgb

Expand Down Expand Up @@ -84,8 +84,8 @@
# can only predict with the best iteration (or the saving iteration)
y_pred = bst.predict(X_test)
# eval with loaded model
rmse_loaded_model = mean_squared_error(y_test, y_pred) ** 0.5
print(f"The RMSE of loaded model's prediction is: {rmse_loaded_model}")
auc_loaded_model = roc_auc_score(y_test, y_pred)
print(f"The ROC AUC of loaded model's prediction is: {auc_loaded_model}")

print('Dumping and loading model with pickle...')
# dump model with pickle
Expand All @@ -97,8 +97,8 @@
# can predict with any iteration when loaded in pickle way
y_pred = pkl_bst.predict(X_test, num_iteration=7)
# eval with loaded model
rmse_pickled_model = mean_squared_error(y_test, y_pred) ** 0.5
print(f"The RMSE of pickled model's prediction is: {rmse_pickled_model}")
auc_pickled_model = roc_auc_score(y_test, y_pred)
print(f"The ROC AUC of pickled model's prediction is: {auc_pickled_model}")

# continue training
# init_model accepts:
Expand Down