---
alias: user-guide-train-ml-model
description: "Train an ML model by transforming data, optimizing metrics, and evaluating its performance"
---

# Train ML Model

:lock: MLModel.**Edit**

## Overview

This operation is used to train an **ML Model**.  

## Setup

No specific setup is required other than to meet the preconditions of the transaction.

## Preconditions

* The **ML Model** exists in the system and a transformation has been applied.

## Sequence of Steps

When the **ML Model** has been created, you can proceed with training.

To conclude the training effectively, the **ML Model** goes through a scaling process.

### Transformations

1. On the top menu, select **Transform**.
2. Choose a field to predict by marking it as a Label.
3. Optionally, you may enable cleaning of `null` values or removing outliers.
4. Optionally, if you do not select a field as a Label, the **ML Model** will automatically be set to `Unsupervised`.
5. Select the percentage of data that should be used to train/validate/test the model.
6. Transform the data.
7. After this scaling operation is successfully completed, the **ML Model** is now ready to train.

When the transformation is applied, the normalized features can be observed in the **Transformed Data** section.

![train_model_05][train_model_step_05]

### Training

1. Select **Train** on the top menu to train the model by choosing how long the AutoML algorithm should run and the metric to optimize.

    ![train_model_01][train_model_step_01]

2. AutoML will automatically train numerous models using different machine learning algorithms with varying hyper-parameters, and then return the best model according to the specified metric. You may also define a timeout for training. If there is no significant improvement on the validation set and the **ML Model** accuracy starts to steadily decrease, the **ML Model** will stop training or it will continue to train until it reaches the timeout you defined.

    ![train_model_02][train_model_step_02]

    !!! info
        In case of unsupervised learning, the model will ask you to define: the number of estimators (trees), the layer depth, and the number of samples.

3. When trained, the model can be tested to evaluate its accuracy.

    ![train_model_06][train_model_step_06]

4. Finally, the model can be deployed so that it can start outputting predictions in real time.

    ![train_model_04][train_model_step_04]

[train_model_step_01]: images/train_ml_model_sep_01.png
[train_model_step_02]: images/train_ml_model_step_02.png
[train_model_step_04]: images/train_ml_model_step_04.png
[train_model_step_05]: images/train_ml_model_step_05.png
[train_model_step_06]: images/train_ml_model_step_06.png