Github user hirakendu commented on a diff in the pull request:
https://github.com/apache/spark/pull/79#discussion_r11229274
--- Diff: mllib/src/main/scala/org/apache/spark/mllib/tree/model/Bin.scala
---
@@ -0,0 +1,33 @@
+/*
+ * 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.mllib.tree.model
+
+import org.apache.spark.mllib.tree.configuration.FeatureType._
+
+/**
+ * Used for "binning" the features bins for faster best split calculation.
For a continuous
+ * feature, a bin is determined by a low and a high "split". For a
categorical feature,
+ * the a bin is determined using a single label value (category).
+ * @param lowSplit signifying the lower threshold for the continuous
feature to be
+ * accepted in the bin
+ * @param highSplit signifying the upper threshold for the continuous
feature to be
+ * accepted in the bin
+ * @param featureType type of feature -- categorical or continuous
+ * @param category categorical label value accepted in the bin
+ */
+case class Bin(lowSplit: Split, highSplit: Split, featureType:
FeatureType, category: Double)
--- End diff --
`Bin` class can be simplified and some members renamed. The `lowSplit`,
`highSplit` can be simplified to the single threshold corresponding to the left
end of the bin range. This can be named to `leftEnd` or `lowEnd`.
It's not clear this class is needed at first place. For categorical
variables, the value itself is the bin index, and for continuous variables,
bins are simply defined by candidate thresholds, in turn defined by quanties.
For every feature id, one can maintain a list of categories and thresholds and
be done. In that case, for continuous features, the position of the threshold
is the bin index.
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