Given a dataset of textual summary of medical queries classified into five different categories. Building our own naïveBayes classifier to predict these categories for future queries.
Apply Laplacian smoothing while learning conditional probabilities to avoid zero values.
Take actual and predicted labels and return precision, recall, f-measure and confusion matrix