When imputing features this dramatically reduces accuracy, as if all the samples are in a single bin of the linearly spaced intervals, then it contributes nothing to the prediction.
When visualizing features, this makes only a small portion of the graph actually be colored.
In order to fix the issue with the graph:
Crop the axis so so that everything below a level of certainty/density removed. This doesn't work so well of the data looks like a '+', as each of the component '-' could be very narrow.
Stretch the axis, so the density governs how much of the axis the feature is given. '+' would look the same if each '-' has the same density.
Neither of these options work for space like 'o'. In this case a transformation would be required to make it easy to view on a graph, such as a Fourier tranform. I am not a fan of this as it would stretch the data. Further investigation is required.
When imputing features this dramatically reduces accuracy, as if all the samples are in a single bin of the linearly spaced intervals, then it contributes nothing to the prediction.
When visualizing features, this makes only a small portion of the graph actually be colored.
In order to fix the issue with the graph:
Crop the axis so so that everything below a level of certainty/density removed. This doesn't work so well of the data looks like a '+', as each of the component '-' could be very narrow.
Stretch the axis, so the density governs how much of the axis the feature is given. '+' would look the same if each '-' has the same density.
Neither of these options work for space like 'o'. In this case a transformation would be required to make it easy to view on a graph, such as a Fourier tranform. I am not a fan of this as it would stretch the data. Further investigation is required.