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 4 mins 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 4 mins 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 4 mins 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 4 mins ago
RocketqRocketq
1235
asked 4 mins ago
RocketqRocketq
1235
asked 4 mins ago
RocketqRocketq
1235
asked 4 mins ago
RocketqRocketq
1235
asked 4 mins ago
RocketqRocketq
1235
1235
add a comment |
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