Preparing Machine Learning Dataset | |
Preparing dataset in Tiki involves creating a new Tiki tracker and adding items to it or using an existing tracker and making any necessary modifications to it. This is an important step because using Machine Learning models to make predictions require the models to be trained beforehand on already existing data. Trackers in Tiki are used for this purpose because of the ease in working with data contained in them just within Tiki, eliminating the need for special tools. |
Creating Dataset | |
You create a dataset in Tiki by simply creating a tracker. Do this by going to the List Trackers page, click on the Create button and fill in the Create tracker form like you would for every other tracker.
Note that the field types you choose will directly affect the user experience during model use. For categorical attributes, consider using field types like Dropdown Tracker Field or Radio Tracker Field to ease user input during usage. See Creating a Tracker for more information on creating trackers. To learn how to add fields to a tracker, see Adding fields to a tracker. |
Adding Samples to Dataset | |
In Tiki, each tracker item represent a sample. You add dataset samples by adding items to the tracker. A simple way to do this is to use the Create Item form displayed after clicking on the Create Item button in the view tracker page.
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Importing Samples | |
If dataset already exist in an external source such as a Comma-Separated Values (CSV) file, you can import samples from it to a tracker in Tiki using Tracker Import Export for this. Say you are working on Divorce Prediction project, you can download the project's dataset into your computer, then using Tracker Tabular in Tiki, import the samples into a tracker.
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Related links | |