> For the complete documentation index, see [llms.txt](https://george-jen.gitbook.io/data-science-and-apache-spark/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://george-jen.gitbook.io/data-science-and-apache-spark/train-validation-split.md).

# Train-Validation Split

In addition to CrossValidator Spark also offers TrainValidationSplit for hyper-parameter tuning. TrainValidationSplit only evaluates each combination of parameters once, as opposed to k times in the case of CrossValidator. It is, therefore, less expensive, but will not produce as reliable results when the training dataset is not sufficiently large.

Unlike CrossValidator, TrainValidationSplit creates a single (training, test) dataset pair. It splits the dataset into these two parts using the trainRatio parameter. For example with trainRatio=0.75, TrainValidationSplit will generate a training and test dataset pair where 75% of the data is used for training and 25% for validation.

Like CrossValidator, TrainValidationSplit finally fits the Estimator using the best ParamMap and the entire dataset.

```
import org.apache.spark.ml.evaluation.RegressionEvaluator
import org.apache.spark.ml.regression.LinearRegression
import org.apache.spark.ml.tuning.{ParamGridBuilder, TrainValidationSplit}

// Prepare training and test data.
val data = spark.read.format("libsvm").load("file:///opt/spark/data/mllib/sample_linear_regression_data.txt")
val Array(training, test) = data.randomSplit(Array(0.9, 0.1), seed = 12345)

val lr = new LinearRegression()
    .setMaxIter(10)

// We use a ParamGridBuilder to construct a grid of parameters to search over.
// TrainValidationSplit will try all combinations of values and determine best model using
// the evaluator.
val paramGrid = new ParamGridBuilder()
  .addGrid(lr.regParam, Array(0.1, 0.01))
  .addGrid(lr.fitIntercept)
  .addGrid(lr.elasticNetParam, Array(0.0, 0.5, 1.0))
  .build()

// In this case the estimator is simply the linear regression.
// A TrainValidationSplit requires an Estimator, a set of Estimator ParamMaps, and an Evaluator.
val trainValidationSplit = new TrainValidationSplit()
  .setEstimator(lr)
  .setEvaluator(new RegressionEvaluator)
  .setEstimatorParamMaps(paramGrid)
  // 80% of the data will be used for training and the remaining 20% for validation.
  .setTrainRatio(0.8)
  // Evaluate up to 2 parameter settings in parallel
  .setParallelism(2)

// Run train validation split, and choose the best set of parameters.
val model = trainValidationSplit.fit(training)

// Make predictions on test data. model is the model with combination of parameters
// that performed best.
model.transform(test)
  .select("features", "label", "prediction")
  .show()
  
/*
Output:
+--------------------+--------------------+--------------------+
|            features|               label|          prediction|
+--------------------+--------------------+--------------------+
|(10,[0,1,2,3,4,5,...|  -23.51088409032297| -1.6659388625179559|
|(10,[0,1,2,3,4,5,...| -21.432387764165806|  0.3400877302576284|
|(10,[0,1,2,3,4,5,...| -12.977848725392104|-0.02335359093652395|
|(10,[0,1,2,3,4,5,...| -11.827072996392571|  2.5642684021108417|
|(10,[0,1,2,3,4,5,...| -10.945919657782932| -0.1631314487734783|
|(10,[0,1,2,3,4,5,...|  -10.58331129986813|   2.517790654691453|
|(10,[0,1,2,3,4,5,...| -10.288657252388708| -0.9443474180536754|
|(10,[0,1,2,3,4,5,...|  -8.822357870425154|  0.6872889429113783|
|(10,[0,1,2,3,4,5,...|  -8.772667465932606|  -1.485408580416465|
|(10,[0,1,2,3,4,5,...|  -8.605713514762092|   1.110272909026478|
|(10,[0,1,2,3,4,5,...|  -6.544633229269576|  3.0454559778611285|
|(10,[0,1,2,3,4,5,...|  -5.055293333055445|  0.6441174575094268|
|(10,[0,1,2,3,4,5,...|  -5.039628433467326|  0.9572366607107066|
|(10,[0,1,2,3,4,5,...|  -4.937258492902948|  0.2292114538379546|
|(10,[0,1,2,3,4,5,...|  -3.741044592262687|   3.343205816009816|
|(10,[0,1,2,3,4,5,...|  -3.731112242951253| -2.6826413698701064|
|(10,[0,1,2,3,4,5,...|  -2.109441044710089| -2.1930034039595445|
|(10,[0,1,2,3,4,5,...| -1.8722161156986976| 0.49547270330052423|
|(10,[0,1,2,3,4,5,...| -1.1009750789589774| -0.9441633113006601|
|(10,[0,1,2,3,4,5,...|-0.48115211266405217| -0.6756196573079968|
+--------------------+--------------------+--------------------+
only showing top 20 rows

*/
```


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://george-jen.gitbook.io/data-science-and-apache-spark/train-validation-split.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.