python - xgb での f スコアの使用

Question

xgb分類子の評価メトリックとしてscikit-learnのf-scoreを使用しようとしています。これが私のコードです：

clf = xgb.XGBClassifier(max_depth=8, learning_rate=0.004,
                            n_estimators=100,
                            silent=False,   objective='binary:logistic',
                            nthread=-1, gamma=0,
                            min_child_weight=1, max_delta_step=0, subsample=0.8,
                            colsample_bytree=0.6,
                            base_score=0.5,
                            seed=0, missing=None)
scores = []
predictions = []
for train, test, ans_train, y_test in zip(trains, tests, ans_trains, ans_tests):
        clf.fit(train, ans_train, eval_metric=xgb_f1,
                    eval_set=[(train, ans_train), (test, y_test)],
                    early_stopping_rounds=900)
        y_pred = clf.predict(test)
        predictions.append(y_pred)
        scores.append(f1_score(y_test, y_pred))

def xgb_f1(y, t):
    t = t.get_label()
    return "f1", f1_score(t, y)

しかし、エラーがあります：Can't handle mix of binary and continuous