# Train, evaluate, interpret and productionize models (Tutorial)

In the previous tutorials, you learned how to use Simple ML to predict missing
values, spot abnormalities in data and to forecast future values. For each of
those tasks, without you knowing, Simple ML trained and applied many machine
learning models. However, it is sometimes necessary to create, evaluate and use
ML models manually. In this tutorial, you will see how to train, evaluate,
analyze and interpret, and finally export an ML model into a Colab.

::: {note}

Colab is a popular platform to write programs using the Python programming
language to do ML learning work. In this example, the model will be trained with
Simple ML for Sheets and then exported to Colab to run inference.

:::

## Training a model

If you have not done it already, make a copy of the
[tutorial sheet](https://docs.google.com/spreadsheets/d/1Ve7umK4W6wBje1QZQVbPIH3djCl2uwfwXvezGliCpss/copy).

The first step is to train your ML model. The tabs "Case #3: Training dataset"
and "Case #3: Evaluation dataset" contains respectively a training and testing
dataset that you will use to train and evaluate a model.

1.  Select the "Case #3: Training dataset" tab.
2.  Under **What do you want to do?** select **Train a model**.
3.  Name your model "My first model".
4.  Under **Label** select **species**. The model will try to predict the
    **species** column.
5.  For now, do not change the **source columns** or the **advanced options**.
6.  Click **Train**.

![Screenshot of the advanced section of the tasks menu](image/tutorial/what_do_you_want_to_do_models.png)

After a few seconds, the model is trained and ready to be used. Press the
**Close** button.

![Screenshot after having trained a model](image/tutorial/train.png)

## Analyse & Interpret the model

It is sometimes interesting to understand what is inside a model. Let's take a
look.

1.  Under **What do you want to do?** select **Understand a model**.
2.  Under **Models** select the model you just trained called "My Model".
3.  Check the box **Include sheet data**.
4.  Click **Understand**.

After a few seconds, the model understanding window appears.

In the **Summary** tab, you can see information about the input features of the
model.

In the **Quality** tab, you can see evaluation metrics about the model. In this
case, the evaluation was computed on a validation dataset extracted
automatically from the training dataset. Different learning algorithms (in the
"advanced options" section) might behave differently.

In the **Dataset** tab, you can see details about the input features of the
model.

In the **Variable importance** tab, you can see how each feature impacts the
model. For example, the feature variable importance `MEAN_DECREASE_IN_ACCURACY`
indicate how much the quality of the model would "drop" if the feature was
removed. The features with the highest feature importance are the most important
to the model.

In the **Predictions** tab, you can see how the prediction of the model is
impacted by different feature values.

![Part of the prediction tab](image/tutorial/understanding.png)

Finally, in the **Plot model** tab, you can see a representation of the model.
Note that only "Decision Tree" models are plotted (to be selected in the
"advanced options" when training the model).

## Evaluate the model

Measuring the quality of a model on a test set (also called "hold-out set") is
critical for measuring the overfitting of the model.

::: {note}

During training, a validation dataset is automatically extracted from the
training dataset to control the training. The evaluation of the validation
dataset is present in the **Model Understanding** window shown in the previous
step. In the current step, the model will be evaluated manually on a new
dataset.

:::

1.  Select the "Case #3: Evaluation dataset" tab.
2.  Under **What do you want to do?** select **Evaluate a model**.
3.  Under **Models** select the model you just trained called "My Model".
4.  Click **Evaluate**.

After a few seconds, the model evaluation window appears. Don't hesitate to
scroll to see evaluation plots.

![Part of the evaluation report](image/tutorial/evaluate.png)

## Export model

For the last step of this tutorial, you will export the model to a Colab.

1.  Select the "Case #3: Evaluation dataset" tab.
2.  Under **What do you want to do?** select **Export a model**.
3.  Under **Models** select the model you just trained called "My Model".
4.  Under **Destination** select **Colab (External)**.
5.  Click **Export**.

After a few seconds, a window showing a snippet of python code appears. You can
paste and run this code in a new
[Colab document](https://colab.research.google.com/). You will see the
predictions of the model.

::: {note}

Model predictions can also be computed in Sheets with Simple ML with the **Make
predictions with a model** task.

:::

![Model exported in colab](image/tutorial/export.png)

## Learn More

If you're interested in learning about the Decision Forests technology that
underlies Simple ML, see the Decision Forests course at
[developers.google.com/machine-learning/decision-forests](https://developers.google.com/machine-learning/decision-forests).