Mapping Dataset

Welcome to the guide for selecting a model and mapping dataset fields for features and target variables in Kranium AI. This guide will walk you through the essential steps to ensure that your machine learning (ML) model is configured correctly and ready for training. Properly selecting a model and mapping the dataset fields are critical steps that ensure your model performs well and provides accurate predictions.

1. Selecting the Model

Steps:

1. Navigate to the Models Section:
   • From the main dashboard, click on the "Models" tab to access the Models section.
   • Select an existing model or create a new model.

2. Mapping Dataset Fields

Steps:

1. Navigate to the Mapping Tab in the model detail page
2. Map Feature Fields:
   • You will be presented with a list of columns (fields) from your dataset. Select the columns that will serve as the input features for your model.
     • Features: These are the input variables that the model will use to make predictions. Select multiple columns if your model requires multiple features.
     • Use checkboxes or a multi-select dropdown to choose the feature columns.
3. Map Target Field:
   • Select the column that will serve as the target variable (output) for your model.
     • Target: This is the variable that the model aims to predict. It should be a single column from your dataset.
     • Use a single-select dropdown to choose the target column.
4. Confirm Mapping:
   • Review your selections for features and target to ensure they are correct. Double confirm the type of problem to ensure the model closely adheres to your requirements..

Selecting the appropriate model and correctly mapping dataset fields for features and target variables are crucial steps in building effective AI models in Kranium AI. By following this guide, you can ensure that your ML model is set up accurately and ready to deliver valuable insights.

For further assistance or advanced techniques, refer to our support resources or contact the Kranium AI support team.