calibrate_model

  • Available in: GBM, DRF
  • Hyperparameter: no

Description

The calibrate_model option allows you to specify Platt scaling in GBM and DRF to calculate calibrated class probabilities. Platt scaling transforms the output of a classification model into a probability distribution over classes. It works by fitting a logistic regression model to a classifier’s scores. Platt scaling will generally not affect the ranking of observations. Logloss, however, will generally improve with Platt scaling.

The calibrate_model option is disabled by default. When enabled, the calibrated probabilities will be appended to the frame with the original prediction.

Note that when this option is enabled, then you must also specify the calibration dataframe (specified with calibrate_frame) that will be used for Platt scaling. A best practice is to split the original dataset into training and calibration sets.

Refer to the following for more information about Platt scaling:

Examples

library(h2o)
h2o.init()

# Import the ecology dataset
ecology.hex <- h2o.importFile("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/ecology_model.csv")

# Convert response column to a factor
ecology.hex$Angaus <- as.factor(ecology.hex$Angaus)

# Split the dataset into training and calibrating datasets
ecology.split <- h2o.splitFrame(ecology.hex, seed = 12354)
ecology.train <- ecology.split[[1]]
ecology.calib <- ecology.split[[2]]

# Introduce a weight column (artificial non-constant) ONLY to the train set (NOT the calibration one)
weights <- c(0, rep(1, nrow(ecology.train) - 1))
ecology.train$weight <- as.h2o(weights)

# Train an H2O GBM Model with the Calibration dataset
ecology.model <- h2o.gbm(x = 3:13, y = "Angaus", training_frame = ecology.train,
                         ntrees = 10,
                         max_depth = 5,
                         min_rows = 10,
                         learn_rate = 0.1,
                         distribution = "multinomial",
                         weights_column = "weight",
                         calibrate_model = TRUE,
                         calibration_frame = ecology.calib
)

predicted <- h2o.predict(ecology.model, ecology.calib)

# View the predictions
predicted
  predict        p0         p1    cal_p0     cal_p1
1       0 0.9201473 0.07985267 0.9415007 0.05849932
2       0 0.9304295 0.06957048 0.9461329 0.05386715
3       0 0.8742164 0.12578357 0.9159100 0.08408999
4       1 0.4877726 0.51222745 0.2896916 0.71030837
5       1 0.4104012 0.58959878 0.1744277 0.82557230
6       1 0.3476665 0.65233355 0.1102849 0.88971514

[256 rows x 5 columns]

import h2o
from h2o.estimators.gbm import H2OGradientBoostingEstimator
h2o.init()

# Import the ecology dataset
ecology = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/ecology_model.csv")

# Convert response column to a factor
ecology['Angaus'] = ecology['Angaus'].asfactor()

# Set the predictors and the response column name
response = 'Angaus'
predictors = ecology.columns[3:13]

# Split into train and calibration sets
train, calib = ecology.split_frame(seed = 12354)

# Introduce a weight column (artificial non-constant) ONLY to the train set (NOT the calibration one)
w = h2o.create_frame(binary_fraction=1, binary_ones_fraction=0.5, missing_fraction=0, rows=744, cols=1)
w.set_names(["weight"])
train = train.cbind(w)

# Train an H2O GBM Model with Calibration
ecology_gbm = H2OGradientBoostingEstimator(ntrees = 10, max_depth = 5, min_rows = 10,
                                           learn_rate = 0.1, distribution = "multinomial",
                                           weights_column = "weight", calibrate_model = True,
                                           calibration_frame = calib)
ecology_gbm.train(x = predictors, y = "Angaus", training_frame = train)

predicted = ecology_gbm.predict(calib)

# View the calibrated predictions appended to the original predictions
predicted
  predict        p0         p1    cal_p0     cal_p1
---------  --------  ---------  --------  ---------
        0  0.881607  0.118393   0.925676  0.0743243
        0  0.917786  0.0822144  0.945076  0.0549236
        0  0.697753  0.302247   0.706711  0.293289
        1  0.538659  0.461341   0.367735  0.632265
        1  0.442108  0.557892   0.197091  0.802909
        1  0.382415  0.617585   0.125879  0.874121
        0  0.923423  0.0765771  0.947633  0.0523671
        0  0.879797  0.120203   0.924555  0.0754445
        0  0.811017  0.188983   0.868916  0.131084
        0  0.709102  0.290898   0.727279  0.272721

[256 rows x 5 columns]