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renanxcortes / count_pixels_green_vectorized.ipynb
Created June 5, 2019 22:35
Zonal Statistics for Raster and Polygons L. Green
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renanxcortes / maxp-BasicTabu-bugs.ipynb
Created August 7, 2019 18:31
maxp-BasicTabu-bugs
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renanxcortes / gist:c7127a23798122896a446098f1803e27
Created June 20, 2020 02:52
automl_with_shapley_snippet1_blogpost
# Metric for binary classification (deviance is the default). Check documentation here http://docs.h2o.ai/h2o/latest-stable/h2o-docs/automl.html
automl_model <- h2o.automl(#x = x,
y = 'y',
balance_classes = TRUE,
training_frame = df_frame_split[[1]],
nfolds = 4,
#validation_frame = df_frame_split[[2]], # read help(h2o.automl) !!!Optional. This argument is ignored unless the user sets nfolds = 0!!!
leaderboard_frame = df_frame_split[[2]],
max_runtime_secs = 60 * 2, # Two minutes
#exclude_algos = "StackedEnsemble", # Global Importance of Stacked models is tricky
# SHAP values: http://docs.h2o.ai/h2o/latest-stable/h2o-r/docs/reference/predict_contributions.H2OModel.html
SHAP_values <- predict_contributions.H2OModel(aml_leader, df_frame_split[[2]])
# Wrangling inspired here: https://bradleyboehmke.github.io/HOML/iml.html
shap_df <- SHAP_values %>%
as.data.frame() %>%
select(-BiasTerm) %>%
gather(feature, shap_value) %>%