1) lasso select some points
2) create new property for every point on the plot:
1 if the point is in the lasso selection
0 if the point is not in the lasso selection
3) create a new frame in h2o-3 that contains the data from all the points on the plot, with the new lassoSelected binary property
4) train some model on that new frame
start with a random forest model
5) poll h2o-3 to find out when training is complete
6) when training is complete, get model metrics from h2o-3
7) parse out training variable importance data from model metrics
8) show top n important variables for lasso selection
depends on #19
design:
1) lasso select some points 2) create new property for every point on the plot:
1
if the point is in the lasso selection0
if the point is not in the lasso selection 3) create a new frame in h2o-3 that contains the data from all the points on the plot, with the new lassoSelected binary property 4) train some model on that new framen
important variables for lasso selection