4 Cluster Configuration Options

While 1-click creation (see Chapter 3 ) provides a convenient and easy way to create a cluster from a dataset, there are cases when you want more control. This section will focus on the options that BigML offers to configure its internal algorithms for clustering.

You can set a number of parameters that affect the way BigML creates models from a dataset. Such parameters can be grouped in two categories:

• Parameters that are permanently associated to the dataset, such as its objective field and preferred fields. Once you provide a value for a dataset’s permanent parameters, they will be used as a default value for the creation of models from that dataset.

• Parameters that only affect the model that is currently being created and that you are expected to set each time. Those include the objective field, included/excluded fields, and a number of configuration options that are described below.

Set a dataset’s permanent parameters by clicking on the edit button that is displayed when you hover on the dataset’s fields. This opens a modal dialog where you can set some of the field properties (See Figure 4.1 ).

Click on the preferred field button to make that field Non-preferred fields.

To access the configuration panel, select the configure clusterz menu option located in the configuration menu of your dataset’s detail view. (See Figure 4.2 .)

When the configuration panel is displayed, you can:

• Select or deselect individual fields for them to be included in or excluded from the cluster computation.

• Set a number of configuration options.

  Note: when the configuration panel is displayed, the edit is not visible, so you cannot set the dataset’s permanent properties.

You can find a detailed explanation of the configuration options in the following sections.