diff --git a/.github/workflows/maven.yml b/.github/workflows/maven.yml
index b1a037dc..107eee33 100644
--- a/.github/workflows/maven.yml
+++ b/.github/workflows/maven.yml
@@ -2,7 +2,7 @@ name: maven
 
 on:
   push:
-    branches: [ '2.0.X', '2.1.X', '2.2.X', master ]
+    branches: [ '2.0.X', '2.1.X', '2.2.X', '2.3.X', master ]
 
 jobs:
   build:
diff --git a/README.md b/README.md
index 4146561d..37544774 100644
--- a/README.md
+++ b/README.md
@@ -73,7 +73,7 @@ Java library and command-line application for converting Apache Spark ML pipelin
       * Trigonometric functions `sin`, `asin`, `sinh`, `cos`, `acos`, `cosh`, `tan`, `atan`, `tanh`.
       * Aggregation functions `greatest` and `least`.
       * RegExp functions `regexp_replace` and `rlike`.
-      * String functions `char_length`, `character_length`, `concat`, `lcase`, `length`, `lower`, `substring`, `trim`, `ucase` and `upper`.
+      * String functions `char_length`, `character_length`, `concat`, `lcase`, `length`, `lower`, `replace`, `substring`, `trim`, `ucase` and `upper`.
       * Type cast functions `boolean`, `cast`, `double`, `int` and `string`.
       * Value functions `in`, `isnan`, `isnull`, `isnotnull`, `negative` and `positive`.
     * [`feature.StandardScalerModel`](https://spark.apache.org/docs/latest/api/java/org/apache/spark/ml/feature/StandardScalerModel.html) (the result of fitting a `feature.StandardScaler`)
@@ -129,7 +129,7 @@ Java library and command-line application for converting Apache Spark ML pipelin
 
 # Prerequisites #
 
-* Apache Spark 1.5.X, 1.6.X, 2.0.X, 2.1.X, 2.2.X, 2.3.X, 2.4.X, 3.0.X, 3.1.X, 3.2.X or 3.3.X.
+* Apache Spark 3.0.X, 3.1.X, 3.2.X, 3.3.X or 3.4.X.
 
 # Installation #
 
@@ -156,7 +156,8 @@ Active development branches:
 | 3.0.X | [`2.0.X`](https://github.com/jpmml/jpmml-sparkml/tree/2.0.X) |
 | 3.1.X | [`2.1.X`](https://github.com/jpmml/jpmml-sparkml/tree/2.1.X) |
 | 3.2.X | [`2.2.X`](https://github.com/jpmml/jpmml-sparkml/tree/2.2.X) |
-| 3.3.X | [`master`](https://github.com/jpmml/jpmml-sparkml/tree/master) |
+| 3.3.X | [`2.3.X`](https://github.com/jpmml/jpmml-sparkml/tree/2.3.X) |
+| 3.4.X | [`master`](https://github.com/jpmml/jpmml-sparkml/tree/master) |
 
 Archived development branches:
 
diff --git a/pmml-sparkml-lightgbm/src/test/java/org/jpmml/sparkml/lightgbm/testing/LightGBMTest.java b/pmml-sparkml-lightgbm/src/test/java/org/jpmml/sparkml/lightgbm/testing/LightGBMTest.java
index 59c48cc8..5a5951d4 100644
--- a/pmml-sparkml-lightgbm/src/test/java/org/jpmml/sparkml/lightgbm/testing/LightGBMTest.java
+++ b/pmml-sparkml-lightgbm/src/test/java/org/jpmml/sparkml/lightgbm/testing/LightGBMTest.java
@@ -18,11 +18,16 @@
  */
 package org.jpmml.sparkml.lightgbm.testing;
 
+import java.util.LinkedHashMap;
+import java.util.List;
+import java.util.Map;
 import java.util.function.Predicate;
 
 import com.google.common.base.Equivalence;
+import org.jpmml.converter.testing.OptionsUtil;
 import org.jpmml.evaluator.ResultField;
 import org.jpmml.evaluator.testing.PMMLEquivalence;
+import org.jpmml.lightgbm.HasLightGBMOptions;
 import org.jpmml.sparkml.testing.SparkMLEncoderBatch;
 import org.jpmml.sparkml.testing.SparkMLEncoderBatchTest;
 import org.junit.AfterClass;
@@ -39,7 +44,24 @@ public LightGBMTest(){
 	public SparkMLEncoderBatch createBatch(String algorithm, String dataset, Predicate<ResultField> columnFilter, Equivalence<Object> equivalence){
 		columnFilter = columnFilter.and(SparkMLEncoderBatchTest.excludePredictionFields());
 
-		return super.createBatch(algorithm, dataset, columnFilter, equivalence);
+		SparkMLEncoderBatch result = new SparkMLEncoderBatch(algorithm, dataset, columnFilter, equivalence){
+
+			@Override
+			public LightGBMTest getArchiveBatchTest(){
+				return LightGBMTest.this;
+			}
+
+			@Override
+			public List<Map<String, Object>> getOptionsMatrix(){
+				Map<String, Object> options = new LinkedHashMap<>();
+
+				options.put(HasLightGBMOptions.OPTION_COMPACT, new Boolean[]{false, true});
+
+				return OptionsUtil.generateOptionsMatrix(options);
+			}
+		};
+
+		return result;
 	}
 
 	@Test
diff --git a/pmml-sparkml-xgboost/src/main/java/org/jpmml/sparkml/xgboost/BoosterUtil.java b/pmml-sparkml-xgboost/src/main/java/org/jpmml/sparkml/xgboost/BoosterUtil.java
index 0b87863e..faff9fba 100644
--- a/pmml-sparkml-xgboost/src/main/java/org/jpmml/sparkml/xgboost/BoosterUtil.java
+++ b/pmml-sparkml-xgboost/src/main/java/org/jpmml/sparkml/xgboost/BoosterUtil.java
@@ -26,9 +26,12 @@
 import java.util.function.Function;
 
 import ml.dmlc.xgboost4j.scala.Booster;
+import ml.dmlc.xgboost4j.scala.spark.params.GeneralParams;
+import org.apache.spark.ml.Model;
+import org.apache.spark.ml.param.shared.HasPredictionCol;
 import org.dmg.pmml.DataType;
+import org.dmg.pmml.Field;
 import org.dmg.pmml.mining.MiningModel;
-import org.jpmml.converter.BinaryFeature;
 import org.jpmml.converter.ContinuousFeature;
 import org.jpmml.converter.Feature;
 import org.jpmml.converter.Schema;
@@ -43,7 +46,9 @@ private BoosterUtil(){
 	}
 
 	static
-	public <C extends ModelConverter<?> & HasXGBoostOptions> MiningModel encodeBooster(C converter, Booster booster, Schema schema){
+	public <M extends Model<M> & HasPredictionCol & GeneralParams, C extends ModelConverter<M> & HasSparkMLXGBoostOptions> MiningModel encodeBooster(C converter, Booster booster, Schema schema){
+		M model = converter.getModel();
+
 		byte[] bytes;
 
 		try {
@@ -60,30 +65,56 @@ public <C extends ModelConverter<?> & HasXGBoostOptions> MiningModel encodeBoost
 			throw new RuntimeException(ioe);
 		}
 
-		Function<Feature, Feature> function = new Function<Feature, Feature>(){
+		Boolean inputFloat = (Boolean)converter.getOption(HasSparkMLXGBoostOptions.OPTION_INPUT_FLOAT, null);
+		if((Boolean.TRUE).equals(inputFloat)){
+			Function<Feature, Feature> function = new Function<Feature, Feature>(){
+
+				@Override
+				public Feature apply(Feature feature){
 
-			@Override
-			public Feature apply(Feature feature){
+					if(feature instanceof ContinuousFeature){
+						ContinuousFeature continuousFeature = (ContinuousFeature)feature;
 
-				if(feature instanceof BinaryFeature){
-					BinaryFeature binaryFeature = (BinaryFeature)feature;
+						DataType dataType = continuousFeature.getDataType();
+						switch(dataType){
+							case INTEGER:
+							case FLOAT:
+								break;
+							case DOUBLE:
+								{
+									Field<?> field = continuousFeature.getField();
 
-					return binaryFeature;
-				} else
+									field.setDataType(DataType.FLOAT);
 
-				{
-					ContinuousFeature continuousFeature = feature.toContinuousFeature(DataType.FLOAT);
+									return new ContinuousFeature(continuousFeature.getEncoder(), field);
+								}
+							default:
+								break;
+						}
+					}
 
-					return continuousFeature;
+					return feature;
 				}
-			}
-		};
+			};
+
+			schema = schema.toTransformedSchema(function);
+		}
+
+		Float missing = model.getMissing();
+		if(missing.isNaN()){
+			missing = null;
+		}
 
 		Map<String, Object> options = new LinkedHashMap<>();
+		options.put(HasXGBoostOptions.OPTION_MISSING, converter.getOption(HasXGBoostOptions.OPTION_MISSING, missing));
 		options.put(HasXGBoostOptions.OPTION_COMPACT, converter.getOption(HasXGBoostOptions.OPTION_COMPACT, false));
+		options.put(HasXGBoostOptions.OPTION_NUMERIC, converter.getOption(HasXGBoostOptions.OPTION_NUMERIC, true));
+		options.put(HasXGBoostOptions.OPTION_PRUNE, converter.getOption(HasXGBoostOptions.OPTION_PRUNE, false));
 		options.put(HasXGBoostOptions.OPTION_NTREE_LIMIT, converter.getOption(HasXGBoostOptions.OPTION_NTREE_LIMIT, null));
 
-		Schema xgbSchema = schema.toTransformedSchema(function);
+		Boolean numeric = (Boolean)options.get(HasXGBoostOptions.OPTION_NUMERIC);
+
+		Schema xgbSchema = learner.toXGBoostSchema(numeric, schema);
 
 		return learner.encodeMiningModel(options, xgbSchema);
 	}
diff --git a/pmml-sparkml-xgboost/src/main/java/org/jpmml/sparkml/xgboost/HasSparkMLXGBoostOptions.java b/pmml-sparkml-xgboost/src/main/java/org/jpmml/sparkml/xgboost/HasSparkMLXGBoostOptions.java
new file mode 100644
index 00000000..074a866b
--- /dev/null
+++ b/pmml-sparkml-xgboost/src/main/java/org/jpmml/sparkml/xgboost/HasSparkMLXGBoostOptions.java
@@ -0,0 +1,27 @@
+/*
+ * Copyright (c) 2023 Villu Ruusmann
+ *
+ * This file is part of JPMML-SparkML
+ *
+ * JPMML-SparkML is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU Affero General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * JPMML-SparkML is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU Affero General Public License for more details.
+ *
+ * You should have received a copy of the GNU Affero General Public License
+ * along with JPMML-SparkML.  If not, see <http://www.gnu.org/licenses/>.
+ */
+package org.jpmml.sparkml.xgboost;
+
+import org.jpmml.sparkml.HasSparkMLOptions;
+import org.jpmml.xgboost.HasXGBoostOptions;
+
+public interface HasSparkMLXGBoostOptions extends HasSparkMLOptions, HasXGBoostOptions {
+
+	String OPTION_INPUT_FLOAT = "input_float";
+}
\ No newline at end of file
diff --git a/pmml-sparkml-xgboost/src/main/java/org/jpmml/sparkml/xgboost/XGBoostClassificationModelConverter.java b/pmml-sparkml-xgboost/src/main/java/org/jpmml/sparkml/xgboost/XGBoostClassificationModelConverter.java
index db600b70..ce59e339 100644
--- a/pmml-sparkml-xgboost/src/main/java/org/jpmml/sparkml/xgboost/XGBoostClassificationModelConverter.java
+++ b/pmml-sparkml-xgboost/src/main/java/org/jpmml/sparkml/xgboost/XGBoostClassificationModelConverter.java
@@ -25,9 +25,8 @@
 import org.jpmml.converter.Schema;
 import org.jpmml.converter.mining.MiningModelUtil;
 import org.jpmml.sparkml.ProbabilisticClassificationModelConverter;
-import org.jpmml.xgboost.HasXGBoostOptions;
 
-public class XGBoostClassificationModelConverter extends ProbabilisticClassificationModelConverter<XGBoostClassificationModel> implements HasXGBoostOptions {
+public class XGBoostClassificationModelConverter extends ProbabilisticClassificationModelConverter<XGBoostClassificationModel> implements HasSparkMLXGBoostOptions {
 
 	public XGBoostClassificationModelConverter(XGBoostClassificationModel model){
 		super(model);
diff --git a/pmml-sparkml-xgboost/src/main/java/org/jpmml/sparkml/xgboost/XGBoostRegressionModelConverter.java b/pmml-sparkml-xgboost/src/main/java/org/jpmml/sparkml/xgboost/XGBoostRegressionModelConverter.java
index 293e40fc..5347782e 100644
--- a/pmml-sparkml-xgboost/src/main/java/org/jpmml/sparkml/xgboost/XGBoostRegressionModelConverter.java
+++ b/pmml-sparkml-xgboost/src/main/java/org/jpmml/sparkml/xgboost/XGBoostRegressionModelConverter.java
@@ -37,9 +37,8 @@
 import org.jpmml.sparkml.PredictionModelConverter;
 import org.jpmml.sparkml.SparkMLEncoder;
 import org.jpmml.sparkml.model.HasPredictionModelOptions;
-import org.jpmml.xgboost.HasXGBoostOptions;
 
-public class XGBoostRegressionModelConverter extends PredictionModelConverter<XGBoostRegressionModel> implements HasXGBoostOptions {
+public class XGBoostRegressionModelConverter extends PredictionModelConverter<XGBoostRegressionModel> implements HasSparkMLXGBoostOptions {
 
 	public XGBoostRegressionModelConverter(XGBoostRegressionModel model){
 		super(model);
diff --git a/pmml-sparkml-xgboost/src/test/java/org/jpmml/sparkml/xgboost/testing/XGBoostTest.java b/pmml-sparkml-xgboost/src/test/java/org/jpmml/sparkml/xgboost/testing/XGBoostTest.java
index 2595a380..63528485 100644
--- a/pmml-sparkml-xgboost/src/test/java/org/jpmml/sparkml/xgboost/testing/XGBoostTest.java
+++ b/pmml-sparkml-xgboost/src/test/java/org/jpmml/sparkml/xgboost/testing/XGBoostTest.java
@@ -18,6 +18,9 @@
  */
 package org.jpmml.sparkml.xgboost.testing;
 
+import java.util.LinkedHashMap;
+import java.util.List;
+import java.util.Map;
 import java.util.function.Predicate;
 
 import com.google.common.base.Equivalence;
@@ -26,11 +29,14 @@
 import org.dmg.pmml.Visitor;
 import org.dmg.pmml.VisitorAction;
 import org.jpmml.converter.testing.Fields;
+import org.jpmml.converter.testing.OptionsUtil;
 import org.jpmml.evaluator.ResultField;
 import org.jpmml.evaluator.testing.FloatEquivalence;
 import org.jpmml.model.visitors.AbstractVisitor;
 import org.jpmml.sparkml.testing.SparkMLEncoderBatch;
 import org.jpmml.sparkml.testing.SparkMLEncoderBatchTest;
+import org.jpmml.sparkml.xgboost.HasSparkMLXGBoostOptions;
+import org.jpmml.xgboost.HasXGBoostOptions;
 import org.junit.AfterClass;
 import org.junit.BeforeClass;
 import org.junit.Test;
@@ -52,6 +58,18 @@ public XGBoostTest getArchiveBatchTest(){
 				return XGBoostTest.this;
 			}
 
+			@Override
+			public List<Map<String, Object>> getOptionsMatrix(){
+				Map<String, Object> options = new LinkedHashMap<>();
+
+				options.put(HasSparkMLXGBoostOptions.OPTION_INPUT_FLOAT, new Boolean[]{false, true});
+
+				options.put(HasXGBoostOptions.OPTION_COMPACT, new Boolean[]{false, true});
+				options.put(HasXGBoostOptions.OPTION_PRUNE, false);
+
+				return OptionsUtil.generateOptionsMatrix(options);
+			}
+
 			@Override
 			public PMML getPMML() throws Exception {
 				PMML pmml = super.getPMML();
@@ -86,6 +104,11 @@ public void evaluateAuto() throws Exception {
 		evaluate("XGBoost", "Auto");
 	}
 
+	@Test
+	public void evaluateHousing() throws Exception {
+		evaluate("XGBoost", "Housing");
+	}
+
 	@Test
 	public void evaluateIris() throws Exception {
 		evaluate("XGBoost", "Iris", new FloatEquivalence(16));
diff --git a/pmml-sparkml-xgboost/src/test/resources/XGBoostAuto.scala b/pmml-sparkml-xgboost/src/test/resources/XGBoostAuto.scala
index 3d329e11..948d09b8 100644
--- a/pmml-sparkml-xgboost/src/test/resources/XGBoostAuto.scala
+++ b/pmml-sparkml-xgboost/src/test/resources/XGBoostAuto.scala
@@ -3,7 +3,7 @@ import java.io.File
 import ml.dmlc.xgboost4j.scala.spark.{TrackerConf, XGBoostRegressor}
 import org.apache.spark.ml.Pipeline
 import org.apache.spark.ml.feature._
-import org.apache.spark.sql.types.StringType
+import org.apache.spark.sql.types.{FloatType, StringType}
 import org.jpmml.sparkml.{DatasetUtil, PipelineModelUtil}
 import org.jpmml.sparkml.xgboost.SparseToDenseTransformer
 
@@ -22,7 +22,7 @@ val assembler = new VectorAssembler().setInputCols(ohe.getOutputCols ++ cont_col
 val sparse2dense = new SparseToDenseTransformer().setInputCol(assembler.getOutputCol).setOutputCol("denseFeatureVec")
 
 val trackerConf = TrackerConf(0, "scala")
-val regressor = new XGBoostRegressor(Map("objective" -> "reg:squarederror", "num_round" -> 101, "num_workers" -> 1, "skip_clean_checkpoint" -> true, "tracker_conf" -> trackerConf)).setLabelCol("mpg").setFeaturesCol(sparse2dense.getOutputCol)
+val regressor = new XGBoostRegressor(Map("objective" -> "reg:squarederror", "num_round" -> 101, "num_workers" -> 1, "tracker_conf" -> trackerConf)).setLabelCol("mpg").setFeaturesCol(sparse2dense.getOutputCol)
 
 val pipeline = new Pipeline().setStages(Array(indexer, ohe, assembler, sparse2dense, regressor))
 val pipelineModel = pipeline.fit(df)
@@ -31,5 +31,6 @@ PipelineModelUtil.storeZip(pipelineModel, new File("pipeline/XGBoostAuto.zip"))
 
 var xgbDf = pipelineModel.transform(df)
 xgbDf = xgbDf.selectExpr("prediction as mpg")
+xgbDf = DatasetUtil.castColumn(xgbDf, "mpg", FloatType)
 
 DatasetUtil.storeCsv(xgbDf, new File("csv/XGBoostAuto.csv"))
diff --git a/pmml-sparkml-xgboost/src/test/resources/XGBoostHousing.scala b/pmml-sparkml-xgboost/src/test/resources/XGBoostHousing.scala
new file mode 100644
index 00000000..a878d3d8
--- /dev/null
+++ b/pmml-sparkml-xgboost/src/test/resources/XGBoostHousing.scala
@@ -0,0 +1,31 @@
+import java.io.File
+
+import ml.dmlc.xgboost4j.scala.spark.{TrackerConf, XGBoostRegressor}
+import org.apache.spark.ml.Pipeline
+import org.apache.spark.ml.feature._
+import org.apache.spark.sql.types.FloatType
+import org.jpmml.sparkml.{DatasetUtil, PipelineModelUtil}
+
+var df = DatasetUtil.loadCsv(spark, new File("csv/Housing.csv"))
+
+DatasetUtil.storeSchema(df, new File("schema/Housing.json"))
+
+val cat_cols = Array("CHAS", "RAD", "TAX")
+val cont_cols = Array("CRIM", "ZN", "INDUS", "NOX", "RM", "AGE", "DIS", "PTRATIO", "B", "LSTAT")
+
+val assembler = new VectorAssembler().setInputCols(cat_cols ++ cont_cols).setOutputCol("featureVector")
+val indexer = new VectorIndexer().setInputCol(assembler.getOutputCol).setOutputCol("catFeatureVector")
+
+val trackerConf = TrackerConf(0, "scala")
+val regressor = new XGBoostRegressor(Map("objective" -> "reg:squarederror", "num_round" -> 101, "num_workers" -> 1, "tracker_conf" -> trackerConf)).setMissing(-1).setLabelCol("MEDV").setFeaturesCol(indexer.getOutputCol)
+
+val pipeline = new Pipeline().setStages(Array(assembler, indexer, regressor))
+val pipelineModel = pipeline.fit(df)
+
+PipelineModelUtil.storeZip(pipelineModel, new File("pipeline/XGBoostHousing.zip"))
+
+var xgbDf = pipelineModel.transform(df)
+xgbDf = xgbDf.selectExpr("prediction as MEDV")
+xgbDf = DatasetUtil.castColumn(xgbDf, "MEDV", FloatType)
+
+DatasetUtil.storeCsv(xgbDf, new File("csv/XGBoostHousing.csv"))
diff --git a/pmml-sparkml-xgboost/src/test/resources/XGBoostIris.scala b/pmml-sparkml-xgboost/src/test/resources/XGBoostIris.scala
index 0eaf95d5..7771b385 100644
--- a/pmml-sparkml-xgboost/src/test/resources/XGBoostIris.scala
+++ b/pmml-sparkml-xgboost/src/test/resources/XGBoostIris.scala
@@ -5,11 +5,16 @@ import org.apache.spark.ml.Pipeline
 import org.apache.spark.ml.feature._
 import org.apache.spark.ml.linalg.Vector
 import org.apache.spark.sql.functions.{lit, udf}
-import org.apache.spark.sql.types.StringType
+import org.apache.spark.sql.types.{DataType, StringType, StructType}
 import org.jpmml.sparkml.{DatasetUtil, PipelineModelUtil}
 
 var df = DatasetUtil.loadCsv(spark, new File("csv/Iris.csv"))
 
+val schema = df.schema
+val floatSchema = DataType.fromJson(schema.json.replaceAll("double", "float"))
+
+df = DatasetUtil.castColumns(df, floatSchema.asInstanceOf[StructType])
+
 DatasetUtil.storeSchema(df, new File("schema/Iris.json"))
 
 val labelIndexer = new StringIndexer().setInputCol("Species").setOutputCol("idx_Species")
diff --git a/pmml-sparkml-xgboost/src/test/resources/csv/Housing.csv b/pmml-sparkml-xgboost/src/test/resources/csv/Housing.csv
new file mode 100644
index 00000000..2c4632b5
--- /dev/null
+++ b/pmml-sparkml-xgboost/src/test/resources/csv/Housing.csv
@@ -0,0 +1,507 @@
+CRIM,ZN,INDUS,CHAS,NOX,RM,AGE,DIS,RAD,TAX,PTRATIO,B,LSTAT,MEDV
+-1.00000,18.00,-1.000,0,-1.0000,6.5750,65.20,4.0900,1,296.0,15.30,-1.00,4.98,24.00
+0.02731,-1.00,7.070,0,0.4690,6.4210,78.90,4.9671,2,242.0,17.80,396.90,9.14,21.60
+0.02729,0.00,-1.000,0,-1.0000,-1.0000,61.10,4.9671,2,242.0,17.80,392.83,4.03,34.70
+-1.00000,0.00,2.180,0,0.4580,6.9980,45.80,6.0622,3,222.0,18.70,394.63,2.94,33.40
+0.06905,0.00,-1.000,0,0.4580,7.1470,-1.00,6.0622,3,222.0,18.70,396.90,5.33,36.20
+-1.00000,0.00,2.180,0,0.4580,6.4300,58.70,6.0622,3,222.0,-1.00,394.12,5.21,28.70
+0.08829,12.50,7.870,0,-1.0000,6.0120,66.60,5.5605,5,311.0,15.20,-1.00,-1.00,22.90
+-1.00000,12.50,7.870,0,-1.0000,6.1720,96.10,5.9505,5,311.0,15.20,396.90,19.15,27.10
+0.21124,12.50,7.870,0,-1.0000,5.6310,100.00,6.0821,5,311.0,15.20,386.63,29.93,16.50
+0.17004,12.50,7.870,0,-1.0000,-1.0000,85.90,6.5921,5,311.0,15.20,386.71,17.10,18.90
+0.22489,12.50,7.870,0,0.5240,6.3770,94.30,6.3467,5,311.0,15.20,392.52,-1.00,15.00
+0.11747,12.50,7.870,0,0.5240,6.0090,82.90,6.2267,5,311.0,15.20,396.90,13.27,18.90
+-1.00000,12.50,7.870,0,0.5240,5.8890,39.00,5.4509,5,311.0,15.20,390.50,15.71,21.70
+-1.00000,0.00,8.140,0,-1.0000,5.9490,61.80,4.7075,4,307.0,21.00,396.90,8.26,20.40
+0.63796,0.00,8.140,0,0.5380,6.0960,84.50,-1.0000,4,307.0,21.00,380.02,10.26,18.20
+0.62739,-1.00,8.140,0,0.5380,5.8340,56.50,4.4986,4,307.0,21.00,395.62,-1.00,19.90
+1.05393,0.00,-1.000,0,-1.0000,-1.0000,29.30,4.4986,4,307.0,21.00,386.85,6.58,23.10
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diff --git a/pmml-sparkml-xgboost/src/test/resources/csv/XGBoostAuto.csv b/pmml-sparkml-xgboost/src/test/resources/csv/XGBoostAuto.csv
index f27b297d..d19571e3 100644
--- a/pmml-sparkml-xgboost/src/test/resources/csv/XGBoostAuto.csv
+++ b/pmml-sparkml-xgboost/src/test/resources/csv/XGBoostAuto.csv
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new file mode 100644
index 00000000..f15540f2
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new file mode 100644
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diff --git a/pmml-sparkml-xgboost/src/test/resources/schema/Iris.json b/pmml-sparkml-xgboost/src/test/resources/schema/Iris.json
index c72cb846..bcb7f35c 100644
--- a/pmml-sparkml-xgboost/src/test/resources/schema/Iris.json
+++ b/pmml-sparkml-xgboost/src/test/resources/schema/Iris.json
@@ -1 +1 @@
-{"type":"struct","fields":[{"name":"Sepal_Length","type":"double","nullable":true,"metadata":{}},{"name":"Sepal_Width","type":"double","nullable":true,"metadata":{}},{"name":"Petal_Length","type":"double","nullable":true,"metadata":{}},{"name":"Petal_Width","type":"double","nullable":true,"metadata":{}},{"name":"Species","type":"string","nullable":true,"metadata":{}}]}
\ No newline at end of file
+{"type":"struct","fields":[{"name":"Species","type":"string","nullable":true,"metadata":{}},{"name":"Sepal_Length","type":"float","nullable":true,"metadata":{}},{"name":"Sepal_Width","type":"float","nullable":true,"metadata":{}},{"name":"Petal_Length","type":"float","nullable":true,"metadata":{}},{"name":"Petal_Width","type":"float","nullable":true,"metadata":{}}]}
\ No newline at end of file
diff --git a/pmml-sparkml/src/main/java/org/jpmml/sparkml/DatasetUtil.java b/pmml-sparkml/src/main/java/org/jpmml/sparkml/DatasetUtil.java
index d9745844..c41db0e6 100644
--- a/pmml-sparkml/src/main/java/org/jpmml/sparkml/DatasetUtil.java
+++ b/pmml-sparkml/src/main/java/org/jpmml/sparkml/DatasetUtil.java
@@ -41,8 +41,11 @@
 import org.apache.spark.sql.catalog.Catalog;
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan;
 import org.apache.spark.sql.execution.QueryExecution;
+import org.apache.spark.sql.types.AtomicType;
 import org.apache.spark.sql.types.BooleanType;
 import org.apache.spark.sql.types.DoubleType;
+import org.apache.spark.sql.types.FloatType;
+import org.apache.spark.sql.types.FractionalType;
 import org.apache.spark.sql.types.IntegralType;
 import org.apache.spark.sql.types.StringType;
 import org.apache.spark.sql.types.StructField;
@@ -178,24 +181,57 @@ public LogicalPlan createAnalyzedLogicalPlan(SparkSession sparkSession, StructTy
 	static
 	public DataType translateDataType(org.apache.spark.sql.types.DataType sparkDataType){
 
-		if(sparkDataType instanceof StringType){
+		if(sparkDataType instanceof AtomicType){
+			return translateAtomicType((AtomicType)sparkDataType);
+		} else
+
+		{
+			throw new IllegalArgumentException("Expected atomic data type, got " + sparkDataType.typeName() + " data type");
+		}
+	}
+
+	static
+	public DataType translateAtomicType(org.apache.spark.sql.types.AtomicType atomicType){
+
+		if(atomicType instanceof StringType){
 			return DataType.STRING;
 		} else
 
-		if(sparkDataType instanceof IntegralType){
-			return DataType.INTEGER;
+		if(atomicType instanceof IntegralType){
+			return translateIntegralType((IntegralType)atomicType);
 		} else
 
-		if(sparkDataType instanceof DoubleType){
-			return DataType.DOUBLE;
+		if(atomicType instanceof FractionalType){
+			return translateFractionalType((FractionalType)atomicType);
 		} else
 
-		if(sparkDataType instanceof BooleanType){
+		if(atomicType instanceof BooleanType){
 			return DataType.BOOLEAN;
 		} else
 
 		{
-			throw new IllegalArgumentException("Expected string, integral, double or boolean data type, got " + sparkDataType.typeName() + " data type");
+			throw new IllegalArgumentException("Expected string, integral, fractional or boolean data type, got " + atomicType.typeName() + " data type");
+		}
+	}
+
+	static
+	public DataType translateIntegralType(IntegralType integralType){
+		return DataType.INTEGER;
+	}
+
+	static
+	public DataType translateFractionalType(FractionalType fractionalType){
+
+		if(fractionalType instanceof FloatType){
+			return DataType.FLOAT;
+		} else
+
+		if(fractionalType instanceof DoubleType){
+			return DataType.DOUBLE;
+		} else
+
+		{
+			throw new IllegalArgumentException("Expected float or double data type, got " + fractionalType.typeName() + " data type");
 		}
 	}
 
diff --git a/pmml-sparkml/src/main/java/org/jpmml/sparkml/ExpressionTranslator.java b/pmml-sparkml/src/main/java/org/jpmml/sparkml/ExpressionTranslator.java
index ed12e626..af69165f 100644
--- a/pmml-sparkml/src/main/java/org/jpmml/sparkml/ExpressionTranslator.java
+++ b/pmml-sparkml/src/main/java/org/jpmml/sparkml/ExpressionTranslator.java
@@ -21,6 +21,7 @@
 import java.util.Iterator;
 import java.util.List;
 import java.util.Objects;
+import java.util.function.Function;
 
 import org.apache.spark.sql.catalyst.expressions.Abs;
 import org.apache.spark.sql.catalyst.expressions.Acos;
@@ -74,6 +75,7 @@
 import org.apache.spark.sql.catalyst.expressions.Sin;
 import org.apache.spark.sql.catalyst.expressions.Sinh;
 import org.apache.spark.sql.catalyst.expressions.Sqrt;
+import org.apache.spark.sql.catalyst.expressions.StringReplace;
 import org.apache.spark.sql.catalyst.expressions.StringTrim;
 import org.apache.spark.sql.catalyst.expressions.Substring;
 import org.apache.spark.sql.catalyst.expressions.Subtract;
@@ -85,6 +87,7 @@
 import org.apache.spark.sql.catalyst.expressions.Upper;
 import org.apache.spark.sql.types.Decimal;
 import org.dmg.pmml.Apply;
+import org.dmg.pmml.Constant;
 import org.dmg.pmml.DataType;
 import org.dmg.pmml.DerivedField;
 import org.dmg.pmml.FieldRef;
@@ -93,6 +96,7 @@
 import org.dmg.pmml.PMMLFunctions;
 import org.jpmml.converter.FieldNameUtil;
 import org.jpmml.converter.PMMLUtil;
+import org.jpmml.converter.TypeUtil;
 import org.jpmml.converter.ValueUtil;
 import org.jpmml.converter.visitors.ExpressionCompactor;
 import scala.Option;
@@ -331,7 +335,7 @@ private org.dmg.pmml.Expression translateInternal(Expression expression){
 					name = FieldNameUtil.create(dataType, ExpressionUtil.format(child));
 				}
 
-				OpType opType = ExpressionUtil.getOpType(dataType);
+				OpType opType = TypeUtil.getOpType(dataType);
 
 				pmmlExpression = AliasExpression.unwrap(pmmlExpression);
 
@@ -469,6 +473,16 @@ private org.dmg.pmml.Expression translateInternal(Expression expression){
 			return PMMLUtil.createApply(PMMLFunctions.MATCHES, translateInternal(left), translateInternal(right));
 		} else
 
+		if(expression instanceof StringReplace){
+			StringReplace stringReplace = (StringReplace)expression;
+
+			Expression srcExpr = stringReplace.srcExpr();
+			Expression searchExpr = stringReplace.searchExpr();
+			Expression replaceExpr = stringReplace.replaceExpr();
+
+			return PMMLUtil.createApply(PMMLFunctions.REPLACE, translateInternal(srcExpr), transformString(translateInternal(searchExpr), ExpressionTranslator::escapeSearchString), transformString(translateInternal(replaceExpr), ExpressionTranslator::escapeReplacementString));
+		} else
+
 		if(expression instanceof StringTrim){
 			StringTrim stringTrim = (StringTrim)expression;
 
@@ -625,6 +639,46 @@ private org.dmg.pmml.Expression translateInternal(Expression expression){
 		}
 	}
 
+	static
+	private String escapeSearchString(String string){
+		return escape(string, "<([{\\^-=$!|]})?*+.>");
+	}
+
+	static
+	private String escapeReplacementString(String string){
+		return escape(string, "\\$");
+	}
+
+	static
+	private String escape(String string, String specialCharacters){
+		StringBuilder sb = new StringBuilder();
+
+		for(int i = 0; i < string.length(); i++){
+			char c = string.charAt(i);
+
+			if(specialCharacters.indexOf(c) > -1){
+				sb.append('\\');
+			}
+
+			sb.append(c);
+		}
+
+		return sb.toString();
+	}
+
+	static
+	private Constant transformString(org.dmg.pmml.Expression pmmlExpression, Function<String, String> function){
+		Constant constant = (Constant)pmmlExpression;
+
+		if(constant.getDataType() != DataType.STRING){
+			throw new IllegalArgumentException();
+		}
+
+		constant.setValue(function.apply((String)constant.getValue()));
+
+		return constant;
+	}
+
 	static
 	private Object toSimpleObject(Object value){
 		Class<?> clazz = value.getClass();
diff --git a/pmml-sparkml/src/main/java/org/jpmml/sparkml/ExpressionUtil.java b/pmml-sparkml/src/main/java/org/jpmml/sparkml/ExpressionUtil.java
index 0b200b88..163671e2 100644
--- a/pmml-sparkml/src/main/java/org/jpmml/sparkml/ExpressionUtil.java
+++ b/pmml-sparkml/src/main/java/org/jpmml/sparkml/ExpressionUtil.java
@@ -19,30 +19,12 @@
 package org.jpmml.sparkml;
 
 import org.apache.spark.sql.catalyst.expressions.Expression;
-import org.dmg.pmml.DataType;
-import org.dmg.pmml.OpType;
 
 public class ExpressionUtil {
 
 	private ExpressionUtil(){
 	}
 
-	static
-	public OpType getOpType(DataType dataType){
-
-		switch(dataType){
-			case STRING:
-				return OpType.CATEGORICAL;
-			case INTEGER:
-			case DOUBLE:
-				return OpType.CONTINUOUS;
-			case BOOLEAN:
-				return OpType.CATEGORICAL;
-			default:
-				throw new IllegalArgumentException("Data type " + dataType + " is not supported");
-		}
-	}
-
 	static
 	public String format(Expression expression){
 		return String.valueOf(expression);
diff --git a/pmml-sparkml/src/main/java/org/jpmml/sparkml/PMMLBuilder.java b/pmml-sparkml/src/main/java/org/jpmml/sparkml/PMMLBuilder.java
index 5eeeb4da..ffffb8c6 100644
--- a/pmml-sparkml/src/main/java/org/jpmml/sparkml/PMMLBuilder.java
+++ b/pmml-sparkml/src/main/java/org/jpmml/sparkml/PMMLBuilder.java
@@ -31,6 +31,7 @@
 import java.util.ListIterator;
 import java.util.Map;
 import java.util.Objects;
+import java.util.Set;
 import java.util.function.Function;
 import java.util.regex.Pattern;
 import java.util.stream.Collectors;
@@ -48,6 +49,7 @@
 import org.apache.spark.ml.tuning.TrainValidationSplitModel;
 import org.apache.spark.sql.Dataset;
 import org.apache.spark.sql.Row;
+import org.apache.spark.sql.types.StructField;
 import org.apache.spark.sql.types.StructType;
 import org.dmg.pmml.DerivedField;
 import org.dmg.pmml.MiningField;
@@ -308,6 +310,23 @@ public File buildFile(File file) throws IOException {
 		return file;
 	}
 
+	public PMMLBuilder extendSchema(Set<String> names){
+		StructType schema = getSchema();
+		PipelineModel pipelineModel = getPipelineModel();
+
+		StructType transformedSchema = pipelineModel.transformSchema(schema);
+
+		for(String name : names){
+			StructField field = transformedSchema.apply(name);
+
+			schema = schema.add(field);
+		}
+
+		setSchema(schema);
+
+		return this;
+	}
+
 	public PMMLBuilder putOption(String key, Object value){
 		return putOptions(Collections.singletonMap(key, value));
 	}
diff --git a/pmml-sparkml/src/main/java/org/jpmml/sparkml/PredictionModelConverter.java b/pmml-sparkml/src/main/java/org/jpmml/sparkml/PredictionModelConverter.java
index dc09d953..c8b4f3e1 100644
--- a/pmml-sparkml/src/main/java/org/jpmml/sparkml/PredictionModelConverter.java
+++ b/pmml-sparkml/src/main/java/org/jpmml/sparkml/PredictionModelConverter.java
@@ -39,9 +39,10 @@
 import org.jpmml.converter.Label;
 import org.jpmml.converter.LabelUtil;
 import org.jpmml.converter.SchemaUtil;
+import org.jpmml.sparkml.model.HasPredictionModelOptions;
 
 abstract
-public class PredictionModelConverter<T extends PredictionModel<Vector, T> & HasLabelCol & HasFeaturesCol & HasPredictionCol> extends ModelConverter<T> {
+public class PredictionModelConverter<T extends PredictionModel<Vector, T> & HasLabelCol & HasFeaturesCol & HasPredictionCol> extends ModelConverter<T> implements HasPredictionModelOptions {
 
 	public PredictionModelConverter(T model){
 		super(model);
diff --git a/pmml-sparkml/src/main/java/org/jpmml/sparkml/SparkMLEncoder.java b/pmml-sparkml/src/main/java/org/jpmml/sparkml/SparkMLEncoder.java
index fb9dc9dd..494eaa0f 100644
--- a/pmml-sparkml/src/main/java/org/jpmml/sparkml/SparkMLEncoder.java
+++ b/pmml-sparkml/src/main/java/org/jpmml/sparkml/SparkMLEncoder.java
@@ -26,14 +26,11 @@
 import java.util.Objects;
 
 import com.google.common.collect.Iterables;
-import org.apache.spark.sql.types.BooleanType;
-import org.apache.spark.sql.types.DoubleType;
-import org.apache.spark.sql.types.IntegralType;
-import org.apache.spark.sql.types.StringType;
 import org.apache.spark.sql.types.StructField;
 import org.apache.spark.sql.types.StructType;
 import org.dmg.pmml.DataField;
 import org.dmg.pmml.DataType;
+import org.dmg.pmml.Field;
 import org.dmg.pmml.Model;
 import org.dmg.pmml.OpType;
 import org.dmg.pmml.PMML;
@@ -45,7 +42,8 @@
 import org.jpmml.converter.Feature;
 import org.jpmml.converter.ModelEncoder;
 import org.jpmml.converter.SchemaUtil;
-import org.jpmml.converter.WildcardFeature;
+import org.jpmml.converter.StringFeature;
+import org.jpmml.converter.TypeUtil;
 import org.jpmml.model.visitors.AbstractVisitor;
 
 public class SparkMLEncoder extends ModelEncoder {
@@ -101,23 +99,7 @@ public List<Feature> getFeatures(String column){
 				dataField = createDataField(name);
 			}
 
-			Feature feature;
-
-			DataType dataType = dataField.requireDataType();
-			switch(dataType){
-				case STRING:
-					feature = new WildcardFeature(this, dataField);
-					break;
-				case INTEGER:
-				case DOUBLE:
-					feature = new ContinuousFeature(this, dataField);
-					break;
-				case BOOLEAN:
-					feature = new BooleanFeature(this, dataField);
-					break;
-				default:
-					throw new IllegalArgumentException("Data type " + dataType + " is not supported");
-			}
+			Feature feature = createFeature(dataField);
 
 			return Collections.singletonList(feature);
 		}
@@ -169,26 +151,28 @@ public DataField createDataField(String name){
 
 		StructField field = schema.apply(name);
 
-		org.apache.spark.sql.types.DataType sparkDataType = field.dataType();
-
-		if(sparkDataType instanceof StringType){
-			return createDataField(name, OpType.CATEGORICAL, DataType.STRING);
-		} else
+		DataType dataType = DatasetUtil.translateDataType(field.dataType());
 
-		if(sparkDataType instanceof IntegralType){
-			return createDataField(name, OpType.CONTINUOUS, DataType.INTEGER);
-		} else
+		OpType opType = TypeUtil.getOpType(dataType);
 
-		if(sparkDataType instanceof DoubleType){
-			return createDataField(name, OpType.CONTINUOUS, DataType.DOUBLE);
-		} else
-
-		if(sparkDataType instanceof BooleanType){
-			return createDataField(name, OpType.CATEGORICAL, DataType.BOOLEAN);
-		} else
+		return createDataField(name, opType, dataType);
+	}
 
-		{
-			throw new IllegalArgumentException("Expected string, integral, double or boolean data type, got " + sparkDataType.typeName() + " data type");
+	public Feature createFeature(Field<?> field){
+		DataType dataType = field.requireDataType();
+		OpType opType = field.requireOpType();
+
+		switch(dataType){
+			case STRING:
+				return new StringFeature(this, field);
+			case INTEGER:
+			case FLOAT:
+			case DOUBLE:
+				return new ContinuousFeature(this, field);
+			case BOOLEAN:
+				return new BooleanFeature(this, field);
+			default:
+				throw new IllegalArgumentException("Data type " + dataType + " is not supported");
 		}
 	}
 
diff --git a/pmml-sparkml/src/main/java/org/jpmml/sparkml/feature/OneHotEncoderModelConverter.java b/pmml-sparkml/src/main/java/org/jpmml/sparkml/feature/OneHotEncoderModelConverter.java
index 5a56981b..3ae9afd5 100644
--- a/pmml-sparkml/src/main/java/org/jpmml/sparkml/feature/OneHotEncoderModelConverter.java
+++ b/pmml-sparkml/src/main/java/org/jpmml/sparkml/feature/OneHotEncoderModelConverter.java
@@ -27,6 +27,7 @@
 import org.jpmml.converter.CategoricalFeature;
 import org.jpmml.converter.Feature;
 import org.jpmml.converter.PMMLEncoder;
+import org.jpmml.converter.SchemaUtil;
 import org.jpmml.sparkml.BinarizedCategoricalFeature;
 import org.jpmml.sparkml.MultiFeatureConverter;
 import org.jpmml.sparkml.SparkMLEncoder;
@@ -80,9 +81,7 @@ public void registerFeatures(SparkMLEncoder encoder){
 		if(outputMode == InOutMode.MULTIPLE){
 			String[] outputCols = transformer.getOutputCols();
 
-			if(outputCols.length != features.size()){
-				throw new IllegalArgumentException("Expected " + outputCols.length + " features, got " + features.size() + " features");
-			}
+			SchemaUtil.checkSize(outputCols.length, features);
 
 			for(int i = 0; i < outputCols.length; i++){
 				String outputCol = outputCols[i];
diff --git a/pmml-sparkml/src/main/java/org/jpmml/sparkml/feature/SQLTransformerConverter.java b/pmml-sparkml/src/main/java/org/jpmml/sparkml/feature/SQLTransformerConverter.java
index b5400390..42d17ec2 100644
--- a/pmml-sparkml/src/main/java/org/jpmml/sparkml/feature/SQLTransformerConverter.java
+++ b/pmml-sparkml/src/main/java/org/jpmml/sparkml/feature/SQLTransformerConverter.java
@@ -33,11 +33,9 @@
 import org.dmg.pmml.OpType;
 import org.dmg.pmml.Visitor;
 import org.dmg.pmml.VisitorAction;
-import org.jpmml.converter.BooleanFeature;
-import org.jpmml.converter.ContinuousFeature;
 import org.jpmml.converter.Feature;
 import org.jpmml.converter.FieldNameUtil;
-import org.jpmml.converter.StringFeature;
+import org.jpmml.converter.TypeUtil;
 import org.jpmml.model.visitors.AbstractVisitor;
 import org.jpmml.sparkml.AliasExpression;
 import org.jpmml.sparkml.DatasetUtil;
@@ -68,32 +66,32 @@ public List<Feature> encodeFeatures(SparkMLEncoder encoder){
 
 		List<Feature> result = new ArrayList<>();
 
-		List<Field<?>> fields = encodeLogicalPlan(encoder, logicalPlan);
-		for(Field<?> field : fields){
-			String name = field.requireName();
-			DataType dataType = field.requireDataType();
-			OpType opType = field.requireOpType();
-
-			Feature feature;
-
-			switch(dataType){
-				case STRING:
-					feature = new StringFeature(encoder, field);
-					break;
-				case INTEGER:
-				case DOUBLE:
-					feature = new ContinuousFeature(encoder, field);
-					break;
-				case BOOLEAN:
-					feature = new BooleanFeature(encoder, field);
-					break;
-				default:
-					throw new IllegalArgumentException("Data type " + dataType + " is not supported");
-			}
+		List<?> objects = encodeLogicalPlan(encoder, logicalPlan);
+		for(Object object : objects){
+
+			if(object instanceof List){
+				List<?> features = (List<?>)object;
+
+				features.stream()
+					.map(Feature.class::cast)
+					.forEach(result::add);
+			} else
+
+			if(object instanceof Field){
+				Field<?> field = (Field<?>)object;
+
+				String name = field.requireName();
+
+				Feature feature = encoder.createFeature(field);
+
+				encoder.putOnlyFeature(name, feature);
 
-			encoder.putOnlyFeature(name, feature);
+				result.add(feature);
+			} else
 
-			result.add(feature);
+			{
+				throw new IllegalArgumentException();
+			}
 		}
 
 		return result;
@@ -105,8 +103,8 @@ public void registerFeatures(SparkMLEncoder encoder){
 	}
 
 	static
-	public List<Field<?>> encodeLogicalPlan(SparkMLEncoder encoder, LogicalPlan logicalPlan){
-		List<Field<?>> result = new ArrayList<>();
+	public List<?> encodeLogicalPlan(SparkMLEncoder encoder, LogicalPlan logicalPlan){
+		List<Object> result = new ArrayList<>();
 
 		List<LogicalPlan> children = JavaConversions.seqAsJavaList(logicalPlan.children());
 		for(LogicalPlan child : children){
@@ -120,6 +118,14 @@ public List<Field<?>> encodeLogicalPlan(SparkMLEncoder encoder, LogicalPlan logi
 			if(pmmlExpression instanceof FieldRef){
 				FieldRef fieldRef = (FieldRef)pmmlExpression;
 
+				if(encoder.hasFeatures(fieldRef.requireField())){
+					List<Feature> features = encoder.getFeatures(fieldRef.requireField());
+
+					result.add(features);
+
+					continue;
+				}
+
 				Field<?> field = ensureField(encoder, fieldRef.requireField());
 				if(field != null){
 					result.add(field);
@@ -142,7 +148,7 @@ public List<Field<?>> encodeLogicalPlan(SparkMLEncoder encoder, LogicalPlan logi
 
 			DataType dataType = DatasetUtil.translateDataType(expression.dataType());
 
-			OpType opType = ExpressionUtil.getOpType(dataType);
+			OpType opType = TypeUtil.getOpType(dataType);
 
 			pmmlExpression = AliasExpression.unwrap(pmmlExpression);
 
diff --git a/pmml-sparkml/src/main/java/org/jpmml/sparkml/testing/SparkMLEncoderBatch.java b/pmml-sparkml/src/main/java/org/jpmml/sparkml/testing/SparkMLEncoderBatch.java
index 9c25eb2d..c4c50777 100644
--- a/pmml-sparkml/src/main/java/org/jpmml/sparkml/testing/SparkMLEncoderBatch.java
+++ b/pmml-sparkml/src/main/java/org/jpmml/sparkml/testing/SparkMLEncoderBatch.java
@@ -114,6 +114,8 @@ public PMML getPMML() throws Exception {
 			pipelineModel = PipelineModelUtil.load(sparkSession, tmpPipelineDir);
 		}
 
+		schema = updateSchema(schema, pipelineModel);
+
 		Dataset<Row> inputDataset;
 
 		try(InputStream is = open(getInputCsvPath())){
@@ -124,7 +126,6 @@ public PMML getPMML() throws Exception {
 			inputDataset = DatasetUtil.loadCsv(sparkSession, tmpCsvFile);
 		}
 
-
 		inputDataset = DatasetUtil.castColumns(inputDataset, schema);
 
 		Map<String, Object> options = getOptions();
@@ -160,6 +161,10 @@ public PMML getPMML() throws Exception {
 		return pmml;
 	}
 
+	protected StructType updateSchema(StructType schema, PipelineModel pipelineModel){
+		return schema;
+	}
+
 	static
 	private File toTmpFile(InputStream is, String prefix, String suffix) throws IOException {
 		File tmpFile = File.createTempFile(prefix, suffix);
diff --git a/pmml-sparkml/src/test/java/org/jpmml/sparkml/ExpressionTranslatorTest.java b/pmml-sparkml/src/test/java/org/jpmml/sparkml/ExpressionTranslatorTest.java
index 9852d27f..25116a21 100644
--- a/pmml-sparkml/src/test/java/org/jpmml/sparkml/ExpressionTranslatorTest.java
+++ b/pmml-sparkml/src/test/java/org/jpmml/sparkml/ExpressionTranslatorTest.java
@@ -252,6 +252,12 @@ public void evaluateStringExpression(){
 
 		checkValue("SPARKSQL", "ucase(\"SparkSql\")");
 		checkValue("SPARKSQL", "upper(\"SparkSql\")");
+
+		checkValue("ABCDEF", "replace(\"ABCabc\", \"abc\", \"DEF\")");
+		checkValue("ABCabc", "replace(\"ABCabc\", \"def\", \"DEF\")");
+		checkValue("ABC", "replace(\"ABCabc\", \"abc\", \"\")");
+
+		checkValue("ABC$abc", "replace(\"ABC.abc\", \".\", \"$\")");
 	}
 
 	@Test
diff --git a/pmml-sparkml/src/test/java/org/jpmml/sparkml/feature/SQLTransformerConverterTest.java b/pmml-sparkml/src/test/java/org/jpmml/sparkml/feature/SQLTransformerConverterTest.java
index 114c09b4..5373e918 100644
--- a/pmml-sparkml/src/test/java/org/jpmml/sparkml/feature/SQLTransformerConverterTest.java
+++ b/pmml-sparkml/src/test/java/org/jpmml/sparkml/feature/SQLTransformerConverterTest.java
@@ -30,7 +30,6 @@
 import org.apache.spark.sql.types.StructType;
 import org.dmg.pmml.DataField;
 import org.dmg.pmml.DerivedField;
-import org.dmg.pmml.Field;
 import org.jpmml.sparkml.ConverterFactory;
 import org.jpmml.sparkml.DatasetUtil;
 import org.jpmml.sparkml.SparkMLEncoder;
@@ -76,7 +75,7 @@ private void checkFields(String sqlStatement, Collection<String> dataFieldNames,
 
 		LogicalPlan logicalPlan = DatasetUtil.createAnalyzedLogicalPlan(SQLTransformerConverterTest.sparkSession, SQLTransformerConverterTest.schema, sqlStatement);
 
-		List<Field<?>> fields = SQLTransformerConverter.encodeLogicalPlan(encoder, logicalPlan);
+		SQLTransformerConverter.encodeLogicalPlan(encoder, logicalPlan);
 
 		Collection<DataField> dataFields = (encoder.getDataFields()).values();
 		for(DataField dataField : dataFields){
diff --git a/pmml-sparkml/src/test/java/org/jpmml/sparkml/testing/ClassificationTest.java b/pmml-sparkml/src/test/java/org/jpmml/sparkml/testing/ClassificationTest.java
index 21953695..80df823f 100644
--- a/pmml-sparkml/src/test/java/org/jpmml/sparkml/testing/ClassificationTest.java
+++ b/pmml-sparkml/src/test/java/org/jpmml/sparkml/testing/ClassificationTest.java
@@ -22,6 +22,8 @@
 import java.util.function.Predicate;
 
 import com.google.common.base.Equivalence;
+import org.apache.spark.ml.PipelineModel;
+import org.apache.spark.sql.types.StructType;
 import org.dmg.pmml.general_regression.GeneralRegressionModel;
 import org.jpmml.converter.testing.Datasets;
 import org.jpmml.converter.testing.Fields;
@@ -61,6 +63,22 @@ public Map<String, Object> getOptions(){
 
 				return options;
 			}
+
+			@Override
+			protected StructType updateSchema(StructType schema, PipelineModel pipelineModel){
+				String algorithm = getAlgorithm();
+				String dataset = getDataset();
+
+				if((MODEL_CHAIN).equals(algorithm) && (AUDIT).equals(dataset)){
+					StructType transformedSchema = pipelineModel.transformSchema(schema);
+
+					return schema
+						.add(transformedSchema.apply("genderPrediction"))
+						.add(transformedSchema.apply("genderProbability"));
+				}
+
+				return super.updateSchema(schema, pipelineModel);
+			}
 		};
 
 		return result;
@@ -88,7 +106,7 @@ public void evaluateLogisticRegressionAudit() throws Exception {
 
 	@Test
 	public void evaluateModelChainAudit() throws Exception {
-		evaluate(MODEL_CHAIN, AUDIT, new PMMLEquivalence(1e-9, 1e-9));
+		evaluate(MODEL_CHAIN, AUDIT, new PMMLEquivalence(3e-9, 3e-9));
 	}
 
 	@Test
diff --git a/pmml-sparkml/src/test/resources/csv/ModelChainAudit.csv b/pmml-sparkml/src/test/resources/csv/ModelChainAudit.csv
index 4a302499..ac26a43a 100644
--- a/pmml-sparkml/src/test/resources/csv/ModelChainAudit.csv
+++ b/pmml-sparkml/src/test/resources/csv/ModelChainAudit.csv
@@ -1,1900 +1,1900 @@
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diff --git a/pmml-sparkml/src/test/resources/main.py b/pmml-sparkml/src/test/resources/main.py
index 20af0c33..048dbc8b 100644
--- a/pmml-sparkml/src/test/resources/main.py
+++ b/pmml-sparkml/src/test/resources/main.py
@@ -44,17 +44,17 @@ def build_modelchain_audit(audit_df, name):
 
 	genderVectorAssembler = VectorAssembler(inputCols = ["Income", "Hours"] + [occupationIndexer.getOutputCol()])
 
-	genderDt = DecisionTreeClassifier(maxDepth = 3, labelCol = genderIndexer.getOutputCol(), featuresCol = genderVectorAssembler.getOutputCol(), rawPredictionCol = "genderRanPrediction", predictionCol = "genderPrediction", probabilityCol = "genderProbability")
+	genderDt = DecisionTreeClassifier(maxDepth = 3, labelCol = genderIndexer.getOutputCol(), featuresCol = genderVectorAssembler.getOutputCol(), predictionCol = "genderPrediction", probabilityCol = "genderProbability")
 
-	genderVectorSlicer = VectorSlicer(indices = [1], inputCol = genderDt.getProbabilityCol(), outputCol = "slicedGenderProbability")
+	genderSqlTransformer = SQLTransformer(statement = "SELECT Adjusted, genderPrediction, genderProbability, Age, Employment, Education, Marital, Deductions FROM __THIS__")
 
-	adjustedFormula = RFormula(formula = "Adjusted ~ genderPrediction + Age + Employment + Education + Marital + Deductions", labelCol = "Adjusted", featuresCol = "adjustedFeatureVector")
+	genderVectorSlicer = VectorSlicer(indices = [1], inputCol = "genderProbability", outputCol = "slicedGenderProbability")
 
-	adjustedVectorAssembler = VectorAssembler(inputCols = [genderVectorSlicer.getOutputCol()] + [adjustedFormula.getFeaturesCol()])
+	adjustedFormula = RFormula(formula = "Adjusted ~ genderPrediction + slicedGenderProbability + Age + Employment + Education + Marital + Deductions", labelCol = "Adjusted", featuresCol = "adjustedFeatureVector")
 
-	adjustedLr = LogisticRegression(labelCol = adjustedFormula.getLabelCol(), featuresCol = adjustedVectorAssembler.getOutputCol())
+	adjustedLr = LogisticRegression(labelCol = adjustedFormula.getLabelCol(), featuresCol = adjustedFormula.getFeaturesCol())
 
-	build_classification(audit_df, Pipeline(stages = [genderIndexer, occupationIndexer, genderVectorAssembler, genderDt, genderVectorSlicer, adjustedFormula, adjustedVectorAssembler, adjustedLr]), adjustedIndexerModel, adjustedLr.getPredictionCol(), adjustedLr.getProbabilityCol(), name)
+	build_classification(audit_df, Pipeline(stages = [genderIndexer, occupationIndexer, genderVectorAssembler, genderDt, genderSqlTransformer, genderVectorSlicer, adjustedFormula, adjustedLr]), adjustedIndexerModel, adjustedLr.getPredictionCol(), adjustedLr.getProbabilityCol(), name)
 
 def build_classification_audit(audit_df, classifier, name):
 	stages = []
diff --git a/pmml-sparkml/src/test/resources/pipeline/ModelChainAudit.zip b/pmml-sparkml/src/test/resources/pipeline/ModelChainAudit.zip
index 8fd58d69..9775942a 100644
Binary files a/pmml-sparkml/src/test/resources/pipeline/ModelChainAudit.zip and b/pmml-sparkml/src/test/resources/pipeline/ModelChainAudit.zip differ