---
title: LinearRegression
description: "LinearRegression为ML API。"
url: https://www.hikunpeng.com/document/detail/zh/kunpengboostkithistory/2003/bds/kunpengbdssparkml_20_0030.html
sourcePath: /source/zh/kunpengboostkithistory/2003/bds/kunpengbdssparkml_20_0030.html
indexId: 3a3d3df0a0cc666673492d8d33f441673de7dada5b7f04c8bd2e6bb535a0e42b73
---
# LinearRegression

LinearRegression为ML API。

| 模型接口类别 | 函数接口 |
| --- | --- |
| ML API | def fit(dataset: Dataset[\_]):LinearRegressionModel |
| ML API | def fit(dataset: Dataset[\_], paramMap: ParamMap): LinearRegressionModel |
| ML API | def fit(dataset: Dataset[\_], firstParamPair: ParamPair[\_], otherParamPairs: ParamPair[\_]\*):LinearRegressionModel |
| ML API | def fit(dataset: Dataset[\_], paramMaps: Array[ParamMap]): Seq[LinearRegressionModel] |


#### ML API

- 功能描述
  传入Dataset格式的样本数据，调用fit接口，输出LinearRegression模型。

- 输入输出

  1. 包名：package org.apache.spark.ml.regression
  2. 类名：LinearRegression
  3. 方法名：fit
  4. 输入：Dataset[_]，训练样本数据，必须字段如下。| Param name | Type(s) | Default | Description |
| --- | --- | --- | --- |
| labelCol | Double | "label" | Label |
| featuresCol | Vector | "features" | 特征标签 |


  5. 算法参数| 算法参数 |
| --- |
| def setRegParam(value: Double): LinearRegression.this.type def setFitIntercept(value: Boolean): LinearRegression.this.type def setStandardization(value: Boolean): LinearRegression.this.type def setElasticNetParam(value: Double): LinearRegression.this.type def setMaxIter(value: Int): LinearRegression.this.type def setTol(value: Double): LinearRegression.this.type def setWeightCol(value: String): LinearRegression.this.type def setSolver(value: String): LinearRegression.this.type def setAggregationDepth(value: Int): LinearRegression.this.type def setLoss(value: String): LinearRegression.this.type def setEpsilon(value: Double): LinearRegression.this.type |


    参数及fit代码接口示例： import org.apache.spark.ml.param.{ParamMap, ParamPair} val linR = new LinearRegression() //定义def fit(dataset: Dataset[_], paramMap: ParamMap) 接口参数 val paramMap = ParamMap(linR.maxIter -> maxIter) .put(linR.regParam, regParam) // 定义def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): 接口参数 val paramMaps: Array[ParamMap] = new Array[ParamMap](2) for (i <- 0 to 2) { paramMaps(i) = ParamMap(linR.maxIter -> maxIter) .put(linR.regParam, regParam) }//对paramMaps进行赋值 // 定义def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*) 接口参数 val regParamPair = ParamPair(linR.regParam, regParam) val maxIterParamPair = ParamPair(linR.maxIter, maxIter) val tolParamPair = ParamPair(linR.tol, tol) // 调用各个fit接口 model = linR.fit(trainingData) model = linR.fit(trainingData, paramMap) models = linR.fit(trainingData, paramMaps) model = linR.fit(trainingData, regParamPair, maxIterParamPair, tolParamPair)

  6. 输出：LinearRegressionModel，模型预测时的输出字段如下。| Param name | Type(s) | Default | Description |
| --- | --- | --- | --- |
| predictionCol | Int | "prediction" | predictionCol |


- 使用样例

```
import org.apache.spark.ml.regression.LinearRegression
// Load training data
val training = spark.read.format("libsvm")
.load("data/mllib/sample_linear_regression_data.txt")
val lr = new LinearRegression()
.setMaxIter(10)
.setRegParam(0.3)
.setElasticNetParam(0.8)
// Fit the model
val lrModel = lr.fit(training)
// Summarize the model over the training set and print out some metrics
val trainingSummary = lrModel.summary
```