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diff --git a/mllib/src/main/scala/org/apache/spark/ml/r/NaiveBayesWrapper.scala b/mllib/src/main/scala/org/apache/spark/ml/r/NaiveBayesWrapper.scala
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+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.r
+
+import org.apache.spark.ml.{Pipeline, PipelineModel}
+import org.apache.spark.ml.attribute.{Attribute, AttributeGroup, NominalAttribute}
+import org.apache.spark.ml.classification.{NaiveBayes, NaiveBayesModel}
+import org.apache.spark.ml.feature.{IndexToString, RFormula}
+import org.apache.spark.sql.DataFrame
+
+private[r] class NaiveBayesWrapper private (
+    pipeline: PipelineModel,
+    val labels: Array[String],
+    val features: Array[String]) {
+
+  import NaiveBayesWrapper._
+
+  private val naiveBayesModel: NaiveBayesModel = pipeline.stages(1).asInstanceOf[NaiveBayesModel]
+
+  lazy val apriori: Array[Double] = naiveBayesModel.pi.toArray.map(math.exp)
+
+  lazy val tables: Array[Double] = naiveBayesModel.theta.toArray.map(math.exp)
+
+  def transform(dataset: DataFrame): DataFrame = {
+    pipeline.transform(dataset).drop(PREDICTED_LABEL_INDEX_COL)
+  }
+}
+
+private[r] object NaiveBayesWrapper {
+
+  val PREDICTED_LABEL_INDEX_COL = "pred_label_idx"
+  val PREDICTED_LABEL_COL = "prediction"
+
+  def fit(formula: String, data: DataFrame, laplace: Double): NaiveBayesWrapper = {
+    val rFormula = new RFormula()
+      .setFormula(formula)
+      .fit(data)
+    // get labels and feature names from output schema
+    val schema = rFormula.transform(data).schema
+    val labelAttr = Attribute.fromStructField(schema(rFormula.getLabelCol))
+      .asInstanceOf[NominalAttribute]
+    val labels = labelAttr.values.get
+    val featureAttrs = AttributeGroup.fromStructField(schema(rFormula.getFeaturesCol))
+      .attributes.get
+    val features = featureAttrs.map(_.name.get)
+    // assemble and fit the pipeline
+    val naiveBayes = new NaiveBayes()
+      .setSmoothing(laplace)
+      .setModelType("bernoulli")
+      .setPredictionCol(PREDICTED_LABEL_INDEX_COL)
+    val idxToStr = new IndexToString()
+      .setInputCol(PREDICTED_LABEL_INDEX_COL)
+      .setOutputCol(PREDICTED_LABEL_COL)
+      .setLabels(labels)
+    val pipeline = new Pipeline()
+      .setStages(Array(rFormula, naiveBayes, idxToStr))
+      .fit(data)
+    new NaiveBayesWrapper(pipeline, labels, features)
+  }
+}