In order to train a machine learning model effectively, the input requirements typically include relevant fields and labels that can guide the learning process. An Incident Field is essential as it provides detailed features or attributes of the incidents that are necessary for the model to make predictions. Additionally, a Verdict Label is crucial as it represents the outcome or classification of each incident, which the model needs to learn from. While having a large number of incidents (like 3000) or knowing the type of the incident might be helpful, they are not strictly necessary for training the model. The key requirements are indeed the features (Incident Field) and the labels (Verdict Label) which correspond to the data attributes and the target variable respectively.