primrose | Primrose modeling framework for simple production models | Machine Learning library

Fuzziness is interpreted as a Levenshtein Edit Distance — the number of one character changes that need to be made to one string to make it the same as another string.

So "sleeping" will match with "seeing" as their is difference of only one character Fuzziness above 2 edit distance is not supported. So value for fuzziness can be only 1 and 2 so it doesn't make sense to allow percentage.

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In all cases the behaviour of fuzziness can be reduced to two integers (as fuzziness above 2 isn't supported anyway): the threshold at which it becomes 1, and at which it becomes 2. Percentage fuzziness used to be supported but was removed, presumably because it suggested that there was more flexibility when really it was just being turned into two thresholds.

Are you suggesting that it should be customisable at the query level, rather than in configuration? If so, some thought would probably need to be put into the syntax, as there could be confusion between settings that define the fuzziness and those that define the length thresholds for fuzziness, which are both integers.

Just set the "next" button to disabled when the first dropdown is changed:

All you're doing is reprinting the variable.

You didn't actually cluster. With as many clusters as data points, you just have the original data... DBSCAN does not make much sense on data with just 7 samples - nothing is "dense" there.

But your actual question is about the standard scaler.

If you encode a categorical attribute as 0 or 1 binary variables, and then apply the standard scaler, 0 will become some negative value, and 1 will be a positive (usually different) value.

Now in your case, there is only one point with that particular value.

That shows why the entire one-hot encoding and standard scaling approach is actually a pretty bad hack. The proper way to use categoricial data with DBSCAN is to either A) define a distance defined on this data - no need to transform the data into vectors - or B) to define appropriate neighbor predicates as detailed in the Generalized DBSCAN follow-up paper for extra control.

I guess you mean creating a bar plot here (with box-plots you usually depict distributions like for examining features etc.; in the iris flower case for example you might want to examine sepal-length in a box plot). If bar plot is what you want, then you can try the following code:

The font size of the icon is larger than the text. So you can reduce your font size of the icon. Also to position the icon to the right you can make it absolute to its container

This are the codes i edited try it. Hope it helps

In that way for example