Configuring experiments

The configuration of experiments consists of selecting the target and the features that the model will use to predict the target. You can also configure a number of optional settings.

To support you in the selection of a target, the historical dataset is analyzed and summary statistics are displayed about each column in the dataset. Several automatic preprocessing steps are applied to the dataset to make sure that only suitable data is included. For more details on the data preprocessing, see Automatic data preparation and transformation.

After running v1, you can create new experiment versions if needed to further refine the model training. For more information, see Refining models.

Requirements and permissions

To learn more about the user requirements for working with ML experiments, see Working with experiments.

Views

The default view is the schema view, where each column in your dataset is represented by a row in the schema with information and statistics. To get more information and sample data for each column you also have the data view. Click and to change between the views.

Click to open or close the Experiment configuration side pane. Here you find information about your experiment and the current configuration.

Selecting a target

The target column contains the values that you want the machine learning model to predict. You can change the target column until you start the first training. After that, it is locked for editing.

Do the following:

• Hover over the column and click the icon that appears.

  The target column is now indicated by and the other available columns are automatically selected as features.

When the target is selected, you can start running the first version of the experiment. Read more in Training experiments. You can do additional configuration at this point—described below—or adjust the configuration after you have reviewed the training results.

Explanations of how your data is being interpreted and processed are shown as you navigate experiment training. For more information, see Common insights found in training data.

Selecting feature columns

With the target set, you can choose which of the other available columns to include in the training of the model. Exclude any features that you don't want to be part of the model. Note that the column will stay in the dataset but will not be used by the training algorithm.

At the top of the Experiment configuration pane, you can see the number of cells in your dataset. If the number exceeds your dataset limit, you can exclude features to get below the limit.

You can select the feature columns in various ways:

• Manually clear the checkboxes for the features you don't want to include.

• Click Exclude all features and then select only the ones you want to include.

• Make a search and exclude or include all features in your filtered search result.

• After you have run the first version of the experiment, you can define the Number of top features to include.

When you select features, they are automatically assigned a feature type. The possible feature types are:

• Categorical

• Numeric

• Date

• Free text

The feature type is assigned based on the data contained in the feature column. If a feature meets certain criteria, it might be staged to become the basis for auto-engineered features. If desired, you can change whether the feature is used for automatic feature engineering. For full details about automatic feature engineering, see Automatic feature engineering.

Certain columns in your dataset may not be selectable as features for your experiment, or may have specific processing applied to them. Explanations of how your data is being interpreted and processed are shown as you navigate experiment training. For more information, see Common insights found in training data.

Selecting algorithms

All available algorithms are included by default and you can exclude any algorithms that you don't want to use. Normally, you would do this as part of the model refinement when you have seen the first training results. Read more in Refining models.

Changing feature types

When a dataset is loaded, the columns are treated as categorical, numeric, date, or free text based on the data type and other characteristics. In some cases, you might want to change this setting.

For example, if the days of the week are represented by the numbers 1-7, each number represents a categorical value. By default, it is treated as a continuous ranked numeric value, so you would need to manually change the configuration to treat it as categorical. You also have the ability to convert a categorical feature type into a numeric feature type.

When a column is identified as containing date and time information, it is used as the basis for new generated auto-engineered features. When this happens, the original column (the parent feature) is treated as having the date feature type. You can change the parent feature from a date feature type to a categorical feature type. However, if you do this, you can no longer use its auto-engineered features in experiment training.

Do the following:

1. In the Feature type column, click .

2. Select a value in the list.

You can see all columns that have a changed feature type on the Experiment configuration pane under Data treatment.

Changing dataset

You can change the training dataset before you run the first experiment version, as well as after running any version.

If you change the dataset before running the first version, you will lose any configuration that you have done prior to changing the dataset.

Do the following:

1. On the Experiment configuration pane under Training data, click Change dataset.

2. Select a new dataset.

For more information about changing and refreshing the dataset during model refinement (after running an experiment version), see Changing and refreshing the dataset.

Configuring hyperparameter optimization

You can optimize the model using hyperparameter optimization. Note that this is an advanced option that could increase the training time significantly. For more information, see Hyperparameter optimization.

Do the following:

1. On the Experiment configuration pane, expand the Model optimization section.

2. Select the Hyperparameter optimization checkbox.

3. Optionally, set a time limit for your optimization. The default time limit is one hour.

Common insights found in training data

Depending on the quality of your dataset, there might be limitations on how you can use specific parts of the data in your experiment configuration. The Insights column in schema view is helpful in identifying particular characteristics of data fields and how they will be processed by machine learning algorithms.

The following table shows possible insights that may be displayed in the schema: