# Scala: Multi-class Classifier

## Spark 中的多類別分類器

對於一個多類別的分類程式已經在 Spark 內的範例中。我們假設所要分類的資料在 `src/main/scala/sample_multiclass_classification_data.txt`

```
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 * this work for additional information regarding copyright ownership.
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// scalastyle:off println
package org.apache.spark.examples.ml

// $example on$
import org.apache.spark.ml.classification.{LogisticRegression, OneVsRest}
import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
// $example off$
import org.apache.spark.sql.SparkSession

/**
 * An example of Multiclass to Binary Reduction with One Vs Rest,
 * using Logistic Regression as the base classifier.
 * Run with
 * {{{
 * ./bin/run-example ml.OneVsRestExample
 * }}}
 */

object OneVsRestExample {
  def main(args: Array[String]) {
    val spark = SparkSession
      .builder
      .appName(s"OneVsRestExample")
      .config("spark.master", "local")
      .getOrCreate()

    // $example on$
    // load data file.
    val inputData = spark.read.format("libsvm")
      .load("src/main/scala/sample_multiclass_classification_data.txt")

    // generate the train/test split.
    val Array(train, test) = inputData.randomSplit(Array(0.8, 0.2))

    // instantiate the base classifier
    val classifier = new LogisticRegression()
      .setMaxIter(10)
      .setTol(1E-6)
      .setFitIntercept(true)

    // instantiate the One Vs Rest Classifier.
    val ovr = new OneVsRest().setClassifier(classifier)

    // train the multiclass model.
    val ovrModel = ovr.fit(train)

    // score the model on test data.
    val predictions = ovrModel.transform(test)

    // obtain evaluator.
    val evaluator = new MulticlassClassificationEvaluator()
      .setMetricName("accuracy")

    // compute the classification error on test data.
    val accuracy = evaluator.evaluate(predictions)
    println(s"Test Error = ${1 - accuracy}")
    // $example off$

    spark.stop()
  }

}
// scalastyle:on println

```

其中，真正處理分類器的是: 

```
    // instantiate the base classifier
    val classifier = new LogisticRegression()
      .setMaxIter(10)
      .setTol(1E-6)
      .setFitIntercept(true)

    // instantiate the One Vs Rest Classifier.
    val ovr = new OneVsRest().setClassifier(classifier)
```

## Logistic Regression

這裡的分類器使用的是 Logistic Regression。Logistic Regression 雖然帶有 Regression (回歸)，卻是一個分類演算法。其主要的想法在於透過一個 Logistic 函數，將回歸值逼近至 0 和 1，再藉由一個閥域的設定，將結果轉為 0 和 1 (binary class) 的分類結果。我們可以用下圖來解釋:

![來自: http://rasbt.github.io/mlxtend/user\_guide/classifier/LogisticRegression/](https://13218333-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-LPzeYWaV4cPH8nI_rTk%2F-LXEsH0wduGS8Xya_dX0%2F-LXEtyfOuX4E5FihO70Z%2Flogistic_regression_schematic.png?alt=media\&token=b1cd0d91-29ef-446c-89c3-e8678c45b857)

其中，在 Activation function (logistic function) 之前，就是一個 linear regression 的模型，藉由找尋 **w** 的參數，找到最小的錯誤。但在分類問題中，藉由 Activation function 和原本的 label (0, 1) 比較計算錯誤，並將 linear regression 的問題，轉換成 classification 的問題。