Probabilities or raw scores#

A classifier usually returns a matrix of probabilities. By default, sklearn-onnx creates an ONNX graph which returns probabilities but it may skip that step and return raw scores if the model implements the method decision_function. Option 'raw_scores' is used to change the default behaviour. Let’s see that on a simple example.

Train a model and convert it#

import numpy
import sklearn
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
import onnxruntime as rt
import onnx
import skl2onnx
from skl2onnx.common.data_types import FloatTensorType
from skl2onnx import convert_sklearn
from sklearn.linear_model import LogisticRegression

iris = load_iris()
X, y =,
X_train, X_test, y_train, y_test = train_test_split(X, y)
clr = LogisticRegression(max_iter=500), y_train)

initial_type = [('float_input', FloatTensorType([None, 4]))]
onx = convert_sklearn(clr, initial_types=initial_type,

Output type#

Let’s confirm the output type of the probabilities is a list of dictionaries with onnxruntime.

sess = rt.InferenceSession(onx.SerializeToString(),
res =, {'float_input': X_test.astype(numpy.float32)})
print("skl", clr.predict_proba(X_test[:1]))
print("onnx", res[1][:2])
skl [[9.94895805e-01 5.10418332e-03 1.21479166e-08]]
onnx [{0: 0.9948958158493042, 1: 0.005104185082018375, 2: 1.2147937766826544e-08}, {0: 0.029782379046082497, 1: 0.9085215926170349, 2: 0.061696045100688934}]

Raw scores and decision_function#

initial_type = [('float_input', FloatTensorType([None, 4]))]
options = {id(clr): {'raw_scores': True}}
onx2 = convert_sklearn(clr, initial_types=initial_type, options=options,

sess2 = rt.InferenceSession(onx2.SerializeToString(),
res2 =, {'float_input': X_test.astype(numpy.float32)})
print("skl", clr.decision_function(X_test[:1]))
print("onnx", res2[1][:2])
skl [[  7.83118948   2.55861193 -10.38980141]]
onnx [{0: 7.831189155578613, 1: 2.558612108230591, 2: -10.389801025390625}, {0: -1.3820686340332031, 1: 2.0358331203460693, 2: -0.6537656784057617}]

Versions used for this example

print("numpy:", numpy.__version__)
print("scikit-learn:", sklearn.__version__)
print("onnx: ", onnx.__version__)
print("onnxruntime: ", rt.__version__)
print("skl2onnx: ", skl2onnx.__version__)
numpy: 1.23.5
scikit-learn: 1.3.dev0
onnx:  1.14.0
onnxruntime:  1.15.0+cpu
skl2onnx:  1.14.0

Total running time of the script: ( 0 minutes 0.042 seconds)

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