A machine learning model can make use of dozens or even thousands of predictors. But which are the most important, and in which direction does a variable change the outcome? For example, does increasing the value of a parcel of land make it more or less likely to have an ADU? This lecture shows how to use feature importances and partial dependence plots to interpret a model’s results.
