how can I integrate continuous data and static data using random forest machine learning model?
I am using random forest regression model to predict groundwater level changes. I am using continuous inputs (timeseries data) such as GRACE, Precipitation, Maximum temperature, Minimum temperature, NDVI as well as static data such as land elevation, hydraulic conductivity, slope, sand percent. When I added static inputs to continuous inputs, the model gave high importance to static inputs and neglected continuous inputs. How I can fix this problem.
What are these Random Forest Decision Boundary indicate?
What insights or implications can be inferred from these images regarding the observed computational behavior?
Randforest, question on “for each node, select features without replacement”
From books (double check with chatGPT), the steps for random forest can be summaried as below: