Creating and configuring the experiment

The first step is to create and configure the experiment. You will use the training dataset you uploaded earlier to train the model until it is ready to be deployed for making predictions.

Creating a new experiment

Do the following:

1. Go to the Create page of the Analytics activity center and select ML experiment.

2. Enter a name for your experiment, for example, Customer churn tutorial.

3. Optionally, add a description and tags.

4. Choose a space for your experiment. It can be your personal space or a shared space.

5. Click Create.

6. Select the training dataset file. This will be either of the following, depending on whether you are working with CSV or QVD:

   • ML - Churn data - training.qvd

   • ML - Churn data - apply.qvd

Reviewing the data

Now you are ready to start configuring your experiment, but before you start, let's have a look at the dataset.

We start out in the Data tab. The default view is the Schema view. Here we can see a table where each row represents a column in your dataset. Statistics and insights have been generated in automatic data preparation. You might have to scroll to the right-hand side of the schema to see the Insights.

We can see that AccountID has been excluded due to high cardinality. This means that the column contains too many unique values. The feature Country has been excluded for the opposite reason: the value is the same for all rows. These two features would not provide any value to the machine learning models.

We can also see that the categorical feature Territory has been impact encoded. Hover over the warning and information icons for more information.

Click Data view. In this view, we can see more information about each column, including sample data.

Selecting a target

We want our machine learning model to predict customer churn, so we select Churned, the final column in the dataset, as our target.

Do the following:

1. Switch back to Schema view.

2. Hover over Churned and click the target icon that appears.

In the experiment configuration panel, we can now see that Churned has been selected. We can also see which features are automatically selected and excluded. Churned is the target so it will not be used as a feature. We can also see that this experiment will be treated as a binary classification problem.

Feature selection and model optimization

By default, the experiment is set to use intelligent model optimization. To confirm, expand Model optimization in the experiment configuration panel. The Intelligent option should be selected.

Feature selection can be manually configured in the Features section of the configuration panel. With intelligent model optimization, feature selection is handled automatically by removal of unhelpful features. With this setting turned on, we can include all available features in the training.

Training the experiment

The configuration is done and we are ready to start the training.

Do the following:

• In the bottom right corner of the experiment window, click Run experiment.

When the experiment has finished running, we can move on to the next step, which is to review the resulting model metrics.