diff --git a/scala-package/core/src/main/scala/org/apache/mxnet/module/BaseModule.scala b/scala-package/core/src/main/scala/org/apache/mxnet/module/BaseModule.scala
index 39da3811f883..7fbdae5b3e21 100644
--- a/scala-package/core/src/main/scala/org/apache/mxnet/module/BaseModule.scala
+++ b/scala-package/core/src/main/scala/org/apache/mxnet/module/BaseModule.scala
@@ -247,23 +247,23 @@ abstract class BaseModule {
 
   /**
    * Run prediction and collect the outputs.
-   * The concatenation process will be like
-   * {{{
-    *   outputBatches = [
-    *      [a1, a2, a3], // batch a
-    *      [b1, b2, b3]  // batch b
-    *   ]
-    *   result = [
-    *     NDArray, // [a1, b1]
-    *     NDArray, // [a2, b2]
-    *     NDArray, // [a3, b3]
-    *   ]
-    * }}}
-   * @param evalData
+   * @param evalData dataIter to do the Inference
    * @param numBatch Default is -1, indicating running all the batches in the data iterator.
    * @param reset Default is `True`, indicating whether we should reset the data iter before start
    *              doing prediction.
    * @return The return value will be a list `[out1, out2, out3]`.
+   *        The concatenation process will be like
+   *        {{{
+   *            outputBatches = [
+   *              [a1, a2, a3], // batch a
+   *              [b1, b2, b3]  // batch b
+   *            ]
+   *            result = [
+   *              NDArray, // [a1, b1]
+   *              NDArray, // [a2, b2]
+   *              NDArray, // [a3, b3]
+   *            ]
+   *        }}}
    *         Where each element is concatenation of the outputs for all the mini-batches.
    */
   def predict(evalData: DataIter, numBatch: Int = -1, reset: Boolean = true)