Validition score while training lower than on final model with xgboost
0
$begingroup$
I have 3 three classes, but my metric is auc, so I have customer eval metric:
# while training eval metric
def custom_eval_metric_class(preds, dtrain):
labels = dtrain.get_label()
labels_processed = [1 if u == 2 else 0 for u in labels]
pred_proba = preds[:, 2]
return 'auc', roc_auc_score(labels_processed, pred_proba)
#final metric function
def roc_auc_score_3class(y_test, y_score):
#print (y_test, 'n', y_score)
metric_auc = roc_auc_score( [1 if v == 2 else 0 for v in y_test],
y_score)
return metric_auc
My target class is 2.
While training model performance on validation reaches .80+ but then on final validation doesnt hit such value. What might be wrong here?
def train_model(X, y, params=None, folds=5, model_type='lgb'):
for fold_n, (train_index, valid_index) in enumerate(folds.split(X, y)):
gc.collect()
print('Fold', fold_n + 1, 'started at', time.ctime())
X_train, X_valid = X.iloc[train_index], X.iloc[valid_index]
y_train, y_valid = y.iloc[train_index], y.iloc[valid_index]
if model_type == 'xgb':
model = xgb.XGBClassifier(params=params, n_estimators = 5000)
model = model.fit(X_train, y_train, eval_set = [(X_valid, y_valid)], early_stopping_rounds=200,
eval_metric = custom_eval_metric_class, verbose = 100)
y_pred_valid = model.predict_proba(X_valid, model.best_ntree_limit)[:, 2]
scores.append(roc_auc_score_3class(y_valid, y_pred_valid))
print('Fold valid roc_auc:', roc_auc_score_3class(y_valid, y_pred_valid))
Will train until validation_0-auc hasn't improved in 200 rounds.
[100] validation_0-merror:0.211905 validation_0-auc:0.790956
[200] validation_0-merror:0.214286 validation_0-auc:0.794158
[300] validation_0-merror:0.210714 validation_0-auc:0.792962
Stopping. Best iteration:
[196] validation_0-merror:0.214286 validation_0-auc:0.796363
Fold valid roc_auc: 0.731813592646
xgboost cross-validation
asked 19 hours ago
RocketqRocketq
1235
$endgroup$
add a comment |
0
$begingroup$
I have 3 three classes, but my metric is auc, so I have customer eval metric:
# while training eval metric
def custom_eval_metric_class(preds, dtrain):
labels = dtrain.get_label()
labels_processed = [1 if u == 2 else 0 for u in labels]
pred_proba = preds[:, 2]
return 'auc', roc_auc_score(labels_processed, pred_proba)
#final metric function
def roc_auc_score_3class(y_test, y_score):
#print (y_test, 'n', y_score)
metric_auc = roc_auc_score( [1 if v == 2 else 0 for v in y_test],
y_score)
return metric_auc
My target class is 2.
While training model performance on validation reaches .80+ but then on final validation doesnt hit such value. What might be wrong here?
def train_model(X, y, params=None, folds=5, model_type='lgb'):
for fold_n, (train_index, valid_index) in enumerate(folds.split(X, y)):
gc.collect()
print('Fold', fold_n + 1, 'started at', time.ctime())
X_train, X_valid = X.iloc[train_index], X.iloc[valid_index]
y_train, y_valid = y.iloc[train_index], y.iloc[valid_index]
if model_type == 'xgb':
model = xgb.XGBClassifier(params=params, n_estimators = 5000)
model = model.fit(X_train, y_train, eval_set = [(X_valid, y_valid)], early_stopping_rounds=200,
eval_metric = custom_eval_metric_class, verbose = 100)
y_pred_valid = model.predict_proba(X_valid, model.best_ntree_limit)[:, 2]
scores.append(roc_auc_score_3class(y_valid, y_pred_valid))
print('Fold valid roc_auc:', roc_auc_score_3class(y_valid, y_pred_valid))
Will train until validation_0-auc hasn't improved in 200 rounds.
[100] validation_0-merror:0.211905 validation_0-auc:0.790956
[200] validation_0-merror:0.214286 validation_0-auc:0.794158
[300] validation_0-merror:0.210714 validation_0-auc:0.792962
Stopping. Best iteration:
[196] validation_0-merror:0.214286 validation_0-auc:0.796363
Fold valid roc_auc: 0.731813592646
xgboost cross-validation
asked 19 hours ago
RocketqRocketq
1235
$endgroup$
add a comment |
0
0
0
$begingroup$
I have 3 three classes, but my metric is auc, so I have customer eval metric:
# while training eval metric
def custom_eval_metric_class(preds, dtrain):
labels = dtrain.get_label()
labels_processed = [1 if u == 2 else 0 for u in labels]
pred_proba = preds[:, 2]
return 'auc', roc_auc_score(labels_processed, pred_proba)
#final metric function
def roc_auc_score_3class(y_test, y_score):
#print (y_test, 'n', y_score)
metric_auc = roc_auc_score( [1 if v == 2 else 0 for v in y_test],
y_score)
return metric_auc
My target class is 2.
While training model performance on validation reaches .80+ but then on final validation doesnt hit such value. What might be wrong here?
def train_model(X, y, params=None, folds=5, model_type='lgb'):
for fold_n, (train_index, valid_index) in enumerate(folds.split(X, y)):
gc.collect()
print('Fold', fold_n + 1, 'started at', time.ctime())
X_train, X_valid = X.iloc[train_index], X.iloc[valid_index]
y_train, y_valid = y.iloc[train_index], y.iloc[valid_index]
if model_type == 'xgb':
model = xgb.XGBClassifier(params=params, n_estimators = 5000)
model = model.fit(X_train, y_train, eval_set = [(X_valid, y_valid)], early_stopping_rounds=200,
eval_metric = custom_eval_metric_class, verbose = 100)
y_pred_valid = model.predict_proba(X_valid, model.best_ntree_limit)[:, 2]
scores.append(roc_auc_score_3class(y_valid, y_pred_valid))
print('Fold valid roc_auc:', roc_auc_score_3class(y_valid, y_pred_valid))
Will train until validation_0-auc hasn't improved in 200 rounds.
[100] validation_0-merror:0.211905 validation_0-auc:0.790956
[200] validation_0-merror:0.214286 validation_0-auc:0.794158
[300] validation_0-merror:0.210714 validation_0-auc:0.792962
Stopping. Best iteration:
[196] validation_0-merror:0.214286 validation_0-auc:0.796363
Fold valid roc_auc: 0.731813592646
xgboost cross-validation
asked 19 hours ago
RocketqRocketq
1235
$endgroup$
I have 3 three classes, but my metric is auc, so I have customer eval metric:
# while training eval metric
def custom_eval_metric_class(preds, dtrain):
labels = dtrain.get_label()
labels_processed = [1 if u == 2 else 0 for u in labels]
pred_proba = preds[:, 2]
return 'auc', roc_auc_score(labels_processed, pred_proba)
#final metric function
def roc_auc_score_3class(y_test, y_score):
#print (y_test, 'n', y_score)
metric_auc = roc_auc_score( [1 if v == 2 else 0 for v in y_test],
y_score)
return metric_auc
My target class is 2.
While training model performance on validation reaches .80+ but then on final validation doesnt hit such value. What might be wrong here?
def train_model(X, y, params=None, folds=5, model_type='lgb'):
for fold_n, (train_index, valid_index) in enumerate(folds.split(X, y)):
gc.collect()
print('Fold', fold_n + 1, 'started at', time.ctime())
X_train, X_valid = X.iloc[train_index], X.iloc[valid_index]
y_train, y_valid = y.iloc[train_index], y.iloc[valid_index]
if model_type == 'xgb':
model = xgb.XGBClassifier(params=params, n_estimators = 5000)
model = model.fit(X_train, y_train, eval_set = [(X_valid, y_valid)], early_stopping_rounds=200,
eval_metric = custom_eval_metric_class, verbose = 100)
y_pred_valid = model.predict_proba(X_valid, model.best_ntree_limit)[:, 2]
scores.append(roc_auc_score_3class(y_valid, y_pred_valid))
print('Fold valid roc_auc:', roc_auc_score_3class(y_valid, y_pred_valid))
Will train until validation_0-auc hasn't improved in 200 rounds.
[100] validation_0-merror:0.211905 validation_0-auc:0.790956
[200] validation_0-merror:0.214286 validation_0-auc:0.794158
[300] validation_0-merror:0.210714 validation_0-auc:0.792962
Stopping. Best iteration:
[196] validation_0-merror:0.214286 validation_0-auc:0.796363
Fold valid roc_auc: 0.731813592646
xgboost cross-validation
xgboost cross-validation
asked 19 hours ago
RocketqRocketq
1235
asked 19 hours ago
RocketqRocketq
1235
asked 19 hours ago
RocketqRocketq
1235
asked 19 hours ago
RocketqRocketq
1235
asked 19 hours ago
RocketqRocketq
1235
1235
add a comment |
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