The dataset’s features are the columns in the dataset matrix. The features are used in the Input or Target block in the Modeling view.
Above each feature, you can see the feature distribution as well as the label and shape.
If you select a feature you can change the label, the feature encoding, e.g., from numeric to categorical, and whether and how it should be normalized.
A combined feature are two or more features that you want to treat in the same way during modeling.
You can only combine features with the same dimensions. For instance, it would not be possible to create a combined feature that includes 28x28x1 pixel images and scalar values.
Example: We want to build a model that predicts the price of a house. Your training data consists of an image of a house and some numeric values about the house including its sale price. Then you should, as an input feature, create a combined feature with all tabular features, excluding the price.
You can do exactly this in the tutorial "Predict California house prices".
Click the New combined feature.
In the pop-up, select the features you want to combine. Note that you can only combine features with the same dimensions.
Name the new combined feature.