Download Researcher`s Workbench User Manual

2| analysis tools that allow researchers to understand
what their models do well and why they fail in
some cases. The fifth, Compare Results, allows
users to look at specific differences between two
different trained models to understand both gaps
in performance as a whole and individually. The
final tab, Predict Labels, allows us to use the resulting trained models to annotate new data that no
humans have labeled.
The simplest workflow, for those with basic
machine learning needs, comes from the first and
third tabs. In each case we progress from an input
data structure to an output data structure:
Documents → Extract Features → Feature Table
Feature Table → Build Model → Trained Model
Each tab in the interface which builds these successive steps is structured with the same basic
workflow, as illustrated in Figure 1.
The top half of each tab is dedicated to configuring your next step of action. As you move from
left to right, your configuration becomes more
fine-grained. You begin on the left by defining what
data you are working with; in the middle you select
which functions within LightSide to use; and on the
right you configure the specific settings you want
to use for that function.
The middle bar in each tab is where you perform
the tab’s action. On the left, in bold, is the button
to begin the action. On the right a progress bar will
appear as the process is running, informing you
that the process is running.
The bottom half of each screen informs you of the
result of the action you perform – descriptions of
the new data object you’ve created. Again, the left
side of the the screen defines which object you’re
looking at, while specific information about that
object is located in the bottom right.
Figure 1. Basic LightSide workflow.