在目前的情况下，我训练的XGBoost模型只为看不见的数据集生成了一个类，尽管我在验证集上收到的准确率约为64%，这对我的用例来说还不错。
在我当前的用例中，我试图为每个编写的文本预测一个目标类，这是18个奇数类，总的数据集大小刚刚超过1000行（非常小），但我的主要问题是为什么XGB只生成一个类。
我使用下面的代码来实现这一点：

#tf-idf verctor representation
 tfidf_vect = TfidfVectorizer(analyzer='word',
                     stop_words=stopwords_custom,
                     max_features=total_features,
                     lowercase=True)
 fitted_vectorizer = tfidf_vect.fit(X_train)
 xtrain_tfidf = fitted_vectorizer.transform(X_train)
 xval_tfidf = fitted_vectorizer.transform(X_val)
 #Running the XGB model
 xgb_params = {"max_depth": (3,5,7),'n_estimators': (50,100,150),
'reg_alpha':[0.1,0.5,1],'reg_lambda':[1,1.5,2],'min_child_weight':[2,4,6]}
 from xgboost import XGBClassifier
 xgb_clf = XGBClassifier()
 grid_xgb = GridSearchCV(estimator=xgb_clf,
                    param_grid=xgb_params,
                    cv=5,
                    n_jobs=-1)
 grid_xgb.fit(xtrain_tfidf,y_train)

 print(grid_xgb.best_params_)
 print(grid_xgb.best_score_)
 #Training the model with best params
 final_xgb = XGBClassifier(max_depth = 5,
                      reg_alpha = 1,
                      reg_lambda = 1,
                      n_estimators = 100,
                      objective='multi:softmax',num_class=18,
                      random_state=42)
 final_xgb.fit(xtrain_tfidf,y_train)
 final_xgb_predict = final_xgb.predict(xval_tfidf)
 xgb_accuracy = metrics.accuracy_score(final_xgb_predict, y_val)
 print ("XGBoost > Accuracy: ", xgb_accuracy)

我哪里做错了？