sort_metric

• Available in: AutoML
• Hyperparameter: no

Description

This option specifies the metric used to sort the Leaderboard by at the end of an AutoML run. Since the “leader model” is the model which has the “best” score on the leaderboard, the leader may change if you change this metric. Most of the time, a Stacked Ensemble will remain in the leader spot even if you change the sorting metric, however, the ranking of the base models will likely change.

Available options for sort_metric include the following:

• AUTO: This defaults to AUC for binary classification, mean_per_class_error for multinomial classification, and deviance for regression.
• deviance (mean residual deviance)
• logloss
• MSE
• RMSE
• MAE
• RMSLE
• AUC (area under the ROC curve)
• AUCPR (area under the Precision-Recall curve)
• mean_per_class_error

For binomial classification choose between AUC, "logloss", "mean_per_class_error", "RMSE", "MSE". For multinomial classification choose between "mean_per_class_error", "logloss", "RMSE", "MSE". For regression choose between "deviance", "RMSE", "MSE", "MAE", "RMLSE".

Example

• r
• python

library(h2o)\
h2o.init()

# Import a sample binary outcome training set into H2O
train <- h2o.importFile("https://s3.amazonaws.com/erin-data/higgs/higgs_train_10k.csv")

# Identify predictors and response
x <- setdiff(names(train), y)
y <- "response"

# For binary classification, response should be a factor
train[,y] <- as.factor(train[,y])

aml <- h2o.automl(x = x, y = y,
                  training_frame = train,
                  max_runtime_secs = 30,
                  sort_metric = "logloss")

# View the AutoML Leaderboard
lb <- aml@leaderboard
lb

#                                                model_id       auc   logloss
# 1    StackedEnsemble_AllModels_0_AutoML_20180604_224722 0.7816898 0.5603201
# 2 StackedEnsemble_BestOfFamily_0_AutoML_20180604_224722 0.7789486 0.5627137
# 3             GBM_grid_0_AutoML_20180604_224722_model_4 0.7772998 0.5643765
# 4             GBM_grid_0_AutoML_20180604_224722_model_0 0.7725441 0.5674791
# 5             GBM_grid_0_AutoML_20180604_224722_model_1 0.7699201 0.5696827
# 6             GBM_grid_0_AutoML_20180604_224722_model_2 0.7700669 0.5707551
#   mean_per_class_error      rmse       mse
# 1            0.3343797 0.4361733 0.1902471
# 2            0.3324797 0.4373470 0.1912724
# 3            0.3267255 0.4380983 0.1919301
# 4            0.3323849 0.4398505 0.1934685
# 5            0.3281922 0.4412005 0.1946579
# 6            0.3438066 0.4412901 0.1947369
#
# [10 rows x 6 columns]

import h2o
from h2o.automl import H2OAutoML
h2o.init()

# Import a sample binary outcome training set into H2O
train = h2o.import_file("https://s3.amazonaws.com/erin-data/higgs/higgs_train_10k.csv")

# Identify predictors and response
x = train.columns
y = "response"
x.remove(y)

# For binary classification, response should be a factor
train[y] = train[y].asfactor()

# Run AutoML for 30 seconds
aml = H2OAutoML(max_runtime_secs = 30, sort_metric = "logloss")
aml.train(x = x, y = y,
          training_frame = train)

# View the AutoML Leaderboard
lb = aml.leaderboard
lb

# model_id                                                    auc    logloss    mean_per_class_error      rmse       mse
# -----------------------------------------------------  --------  ---------  ----------------------  --------  --------
# StackedEnsemble_AllModels_0_AutoML_20180605_001915     0.783325   0.558667                0.313514  0.435453  0.18962
# StackedEnsemble_BestOfFamily_0_AutoML_20180605_001915  0.780711   0.56117                 0.317926  0.436721  0.190726
# GBM_grid_0_AutoML_20180605_001915_model_0              0.777781   0.562631                0.330729  0.437568  0.191466
# GBM_grid_0_AutoML_20180605_001915_model_1              0.775025   0.56548                 0.329763  0.438794  0.19254
# GBM_grid_0_AutoML_20180605_001915_model_2              0.769711   0.569923                0.334983  0.441401  0.194835
# GBM_grid_0_AutoML_20180605_001915_model_3              0.761701   0.579553                0.345298  0.445009  0.198033
# DRF_0_AutoML_20180605_001915                           0.743439   0.594876                0.35481   0.452465  0.204725
# XRT_0_AutoML_20180605_001915                           0.735455   0.605614                0.370628  0.455573  0.207547
# GLM_grid_0_AutoML_20180605_001915_model_0              0.68048    0.639935                0.393134  0.473447  0.224152
#
# [9 rows x 6 columns]