Your data contains 7621 records and 3873 of them are unique records.
When there is duplicates in different folds, cross-validation fails. And these duplicates causes to high train&test set performance metrics. Removing the duplicates decreases the test set's accuracy and f-1 scores:
import pandas as pd
from sklearn.model_selection import KFold,...
It should work: the variable is ordinal so using numerical values makes sense.
So there's a bug somewhere, here are a few suggestions of things to look at:
Possibly a type conversion error somewhere: make sure the variable is interpreted as numerical.
Check whether the model actually uses the variable: if not then it's likely some type error; if yes then I ...
Very new release:
The main objective of the package is to allow creating decision trees that are better in some aspects than trees made by greedy algorithms.
The creation of trees is made by genetic algorithm. In order to achive as fast as possible evolution of trees the most time consuming components are wrtitten in Cython. Also there are ...