Open zhangpengshan opened 7 years ago
Like grid search in h2o:
hyper_parameters = {'ntrees':[50],
'max_depth':list(range(5,10,2)),
'sample_rate':[x * 0.1 for x in range(8, 11, 2)],
'nbins':list(range(20,50,10)),
'min_rows':list(range(10,40,10)),
'histogram_type': ["UniformAdaptive","QuantilesGlobal","RoundRobin"]
}
Like number of binning: 20 or 30, missing value processing type, these should impact model performance in triaining,
how to add such parameters to grid search?