# tModelEncoder

Performs featurization operations to transform data into the format expected by the model training components such as tLogisticRegressionModel or tKMeansModel.

tModelEncoder receives data from its preceding components, applies a wide range of feature processing algorithms to transform given columns of this data and sends the result to the model training component that follows to eventually train and create a predictive model.

In local mode, Apache Spark 3.0.x and later versions are supported.

This component is not shipped with your Talend Studio by default. You need to install it using the Feature Manager. For more information, see [Installing features using the Feature Manager](https://help.qlik.com/talend/r/en-US/8.0/studio-user-guide/install-features-to-talend-studio).

### In this section

* [tModelEncoder properties for Apache Spark Batch](https://help.qlik.com/talend/en-US/components/8.0/machine-learning/tmodelencoder-properties-for-apache-spark-batch)
* [tModelEncoder properties for Apache Spark Streaming (deprecated)](https://help.qlik.com/talend/en-US/components/8.0/machine-learning/tmodelencoder-properties-for-apache-spark-streaming)

**Related tasks**

* [Creating a classification model to filter spam](https://help.qlik.com/talend/en-US/components/8.0/machine-learning/classification-model)
* [Modeling the accident-prone areas in a city](https://help.qlik.com/talend/en-US/components/8.0/machine-learning/tmodelencoder-tkmeansmodel-ts3configuration-modeling-accident-prone-areas-in-city-spark-batch-component-in-this "In this scenario, the tKMeansModel component is used to analyze a set of sample geographical data about the destination of ambulances in a city in order to model the accident-prone areas.")