In my research experiments participants were asked to make many binary choices between "heads" and "tails" of a coin (for more details, see our publication here). Participants were specifically instructed to make these choices randomly, without following any pattern. We looked at how well people performed the task and explored what happens in their brain while doing it (publication coming soon 😎).
I decided to build a similar experiment in a web application using streamlit, which is handy a Python library that turns data scripts into shareable web apps. The program (I call it ✨choice predictor✨) asks users to make binary choices (1 or 0), and the algorithm learns from these choices to predict the next one.
- User can choose the algorithm that will be used for the prediction. Possible choices: xgboost, logistic regression or random forest. The default algorithm is xgboost and will be used if no choice is made.
- User makes a set amount of choices by pressing one of two buttons to create an initial training set (number of choices is determined by
initSeqLen) - As soon as this initial set is gathered, the algorithm does a parameter search to find optimal number of features.
- This optimal number of features is used to make a prediction for the user's next choice.
- After every X steps (X is determined by the
interValToTestconstant, here it's 1), a new parameter search is run and the newly found optimal parameter is used in the updated model for the following predictions
Additionally some metrics are displayed, e.g. the % of correct predictions, with an indicator of whether and by how much the percentage changed compared to the previous one. A line chart illustrates the percentage metric over time.
The app is deployed and accesible under this link: https://choicepredictor.streamlit.app/