PMML output

pmml_output.xml

<?xml version="1.0" encoding="UTF-8" standalone="yes"?>
<PMML xmlns="http://www.dmg.org/PMML-4_3" xmlns:data="http://jpmml.org/jpmml-model/InlineTable" version="4.3">
	<Header>
		<Application name="JPMML-SkLearn" version="1.5.6"/>
		<Timestamp>2018-09-08T11:13:03Z</Timestamp>
	</Header>
	<MiningBuildTask>
		<Extension>PMMLPipeline(steps=[('mapper', DataFrameMapper(default=False, df_out=False,
        features=[(['mass'], FunctionTransformer(accept_sparse=False, func=&lt;ufunc 'log1p'&gt;,
          inv_kw_args=None, inverse_func=None, kw_args=None,
          pass_y='deprecated', validate=True)), (['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age'], None)],
        input_df=False, sparse=False)),
       ('classifier', LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,
          verbose=0, warm_start=False))])</Extension>
	</MiningBuildTask>
	<DataDictionary>
		<DataField name="label" optype="categorical" dataType="integer">
			<Value value="0"/>
			<Value value="1"/>
		</DataField>
		<DataField name="mass" optype="continuous" dataType="double"/>
		<DataField name="preg" optype="continuous" dataType="double"/>
		<DataField name="plas" optype="continuous" dataType="double"/>
		<DataField name="pres" optype="continuous" dataType="double"/>
		<DataField name="skin" optype="continuous" dataType="double"/>
		<DataField name="test" optype="continuous" dataType="double"/>
		<DataField name="pedi" optype="continuous" dataType="double"/>
		<DataField name="age" optype="continuous" dataType="double"/>
	</DataDictionary>
	<TransformationDictionary>
		<DerivedField name="log1p(mass)" optype="continuous" dataType="double">
			<Apply function="x-ln1p">
				<FieldRef field="mass"/>
			</Apply>
		</DerivedField>
	</TransformationDictionary>
	<RegressionModel functionName="classification" normalizationMethod="logit">
		<MiningSchema>
			<MiningField name="label" usageType="target"/>
			<MiningField name="mass"/>
			<MiningField name="preg"/>
			<MiningField name="plas"/>
			<MiningField name="pres"/>
			<MiningField name="skin"/>
			<MiningField name="test"/>
			<MiningField name="pedi"/>
			<MiningField name="age"/>
		</MiningSchema>
		<Output>
			<OutputField name="probability(0)" optype="continuous" dataType="double" feature="probability" value="0"/>
			<OutputField name="probability(1)" optype="continuous" dataType="double" feature="probability" value="1"/>
		</Output>
		<RegressionTable intercept="-3.3259427586269217" targetCategory="1">
			<NumericPredictor name="log1p(mass)" coefficient="-1.843339687128191"/>
			<NumericPredictor name="preg" coefficient="0.06489477494235751"/>
			<NumericPredictor name="plas" coefficient="0.029913486211846644"/>
			<NumericPredictor name="pres" coefficient="-0.014330833443219983"/>
			<NumericPredictor name="skin" coefficient="-0.0035644630076708873"/>
			<NumericPredictor name="test" coefficient="-3.9194089727002555E-4"/>
			<NumericPredictor name="mass" coefficient="0.16331077682887302"/>
			<NumericPredictor name="pedi" coefficient="0.16365918512828412"/>
			<NumericPredictor name="age" coefficient="0.02584172148238294"/>
		</RegressionTable>
		<RegressionTable intercept="0.0" targetCategory="0"/>
	</RegressionModel>
</PMML>