This paper proposes a new information visualisation interface to help with the reading and improvement of Biochips. The interface serves two main groups of bio-chip end users. Biologists who use the chips to detect biochemical substances can use the interface to read chips and determine the reliability of readings. It also helps bio-chip developers to design and train classification models by seeing how well the different biosensors work and how the data fits their model. The interface proposed uses a Random Forest classifier and visualises the classification to provide a better understanding of how the data is classified by showing how it fits different classifications and how changes in attribute values can affect the classification. The interface also allows model-developers to interact to see how their model works for different attribute values, and shows them how new data (sent by model-users) fits into their classification model. This allow the biochip designers to detect how their model may be limited so they can retrain the model accordingly. The particular challenge with this project is how we manage and visualise uncertainty related to bio-sensor readings (that can be resultant from the manufacturing process and environmental factors) and the machine learning models, so that biologists can account for this when designing or using chips. Overall, our interface demonstrates the potential of information visualisation to be used to allow developers and model-users to better understand the effectiveness of classification models for their data, as well as the potential of collaborative interfaces to help them work together to build more effective supervised classification models.