I have a model based on Naive-bays classifier (multinomial Naive bays) that i have fitted on data set with just one feature ( categorical observation) and a label :

**observation** ; **label**

funny smell ; Acanthamoeba Infection

fever ; ear infection

fever ; cancer

nose bleed ; Acanthamoeba Infection

now i have received new data samples that include n copies of the observation**observation** ; **label**

fever + nose bleed + leg pain ; head trauma .

is there a way to incorporate the new samples into the model ? i’m using scikit learn implementation for the naive bays . i was thinking about using the conditional probability of knowing the relations that each single observation have with label and how each observation contribute to my current knowledge , but not sure how can i use it with sickit learn implementation.