in src/ppxgboost/PPBooster.py [0:0]
def predict_single_input_binary(trees, vector, default_base_score=0.5):
"""
return a prediction on a single vector.
:param trees: a list of trees (model represenation)
:param vector: a single input vector
:param default_base_score: a default score is 0.5 (global bias)
:return: the predicted score
"""
predict_sum_score = default_base_score
for t in trees:
score = t.eval(vector)
predict_sum_score += score
return predict_sum_score