XGBoost Multilabel Classification

Flu Shot Learning
1

Hello everyone,

In the multilabel classification for predicting h1n1 and seasonal vaccine, one of the class probability is always predict as ‘always greater than 0.5’ (which means label 1). I am not sure why this is happening.

[0.8590254 0.43965816]
[0.86548555 0.42004538]
[0.8504172 0.53533596]
[0.84696984 0.40868753]
[0.84735835 0.43021488]
[0.64695126 0.75616175]
[0.8121675 0.57143956]
[0.77992 0.5903182 ]
[0.86847746 0.3757938 ]
[0.75904495 0.692877 ]
[0.72046316 0.7369681 ]
[0.8597982 0.42459705]

Any insights would be much appreciated.

Here’s the my model:

def fit_model(X_train, Y_train):
    clf = OneVsRestClassifier(XGBClassifier(n_jobs=-1,
                                            silent=0,
                                            verbose=True,
                                            # eval_metric = ["auc","error"],
                                            objective='multi:softprob',
                                            # nclasses=2,
                                            num_class=2,
                                            learning_rate=0.05,
                                            colsample_bylevel=0.20,
                                            colsample_bynode=0.20,
                                            colsample_bytree=0.20,
                                            min_child_weight=5,
                                            max_depth=12,
                                            subsample=1,
                                            n_estimators=100))
    mlb = MultiLabelBinarizer()
    Y_train = mlb.fit_transform(Y_train)
    clf.fit(X_train, Y_train)
    return clf
2

Try to use MultiOutputClassifier() instead of MultiLabelBinarizer()
https://scikit-learn.org/stable/modules/generated/sklearn.multioutput.MultiOutputClassifier.html?highlight=multioutputclassifier#sklearn.multioutput.MultiOutputClassifier