calibrate_frame

  • Available in: GBM, DRF
  • Hyperparameter: no

Description

The calibrate_frame specifies the calibration frame that will be used for Platt scaling. This option is required if calibrate_model is enabled.

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.

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]