Minor update: broken dependencies fixed

v0.9.4

update v0.9.4 - broken dependency fix

Fixed DT's nondeterministic behavior

As described in scikit-learn/scikit-learn#8443, the Scikit-Learn implementation for the decision tree algorithm by default is not deterministic as it should be. This is due to a design choice here](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/tree/_splitter.pyx#L381-L401) where even if max_features = n_features, the algorithm still randomly samples up to max_features.
To address this unexpected behavior, the internal random state of the DecisionTreeClassifier in Araucana has been fixed to 1. The global randomness of Araucana (e.g., during oversampling) can still be controlled with the 'seed' parameter.

v0.9.2

Merge branch 'master' of https://github.com/detsutut/AraucanaXAI

Pre-release: minor refactoring + oversampling bug fix

v0.9.1

Merge branch 'master' of https://github.com/detsutut/AraucanaXAI

Pre-release: new oversampling strategies and minor general improvements

v0.9

Update README.md

Pre-release: parameter tuning

v0.0.5

Update README.md

Pre-release: continuous-only datasets handling

v0.0.4 - handle datasets without cat features, solved module import i…

…ssue

Pre-release: basic implementation

v0.0.2 - removed internal plotting + toy dataset + refactoring + upda…

…ted example