bestree 0.1.0

A Python package for finding the best decision tree parameters.

bestree

Under construction.

---

Developed by CodingLive from ProtDos (c) 2022

##Installation

pip install bestree

Requirements

• Python (>= 3.8)

• NumPy (>= 1.17.3)

• ScikitLearn

• Pandas

Usage

All best features

from bestree import best_all

from sklearn.tree import DecisionTreeClassifier



from sklearn.datasets import make_blobs # To create random X and y data

X, y = make_blobs(n_samples=100, n_features=2, centers=3, random_state=0)



criterion, max_depths, split, state = best_all(X, y)



clf = DecisionTreeClassifier(max_depth=max_depths, criterion=criterion, min_samples_split=split, random_state=state)

# your script

Choosing the best criterion

from bestree import best_criterion #For pandas objects

from bestree import best_criterion_norm #For other objects

from sklearn.tree import DecisionTreeClassifier



from sklearn.datasets import make_blobs # To create random X and y data

X, y = make_blobs(n_samples=100, n_features=2, centers=3, random_state=0)



criterion = best_criterion_norm(X, y)

print(criterion)



clf = DecisionTreeClassifier(criterion=criterion)

# your script

Choosing the best max_depth value

from bestree import best_depth

from sklearn.tree import DecisionTreeClassifier



from sklearn.datasets import make_blobs # To create random X and y data

X, y = make_blobs(n_samples=100, n_features=2, centers=3, random_state=0)



max_depth = best_depth(X, y, criterion="gini")



clf = DecisionTreeClassifier(max_depth=max_depth)

# your script

Choosing the best splitter

from bestree import best_splitter

from sklearn.tree import DecisionTreeClassifier



from sklearn.datasets import make_blobs # To create random X and y data

X, y = make_blobs(n_samples=100, n_features=2, centers=3, random_state=0)



max_depths = 3

criterion = "gini"



split = best_splitter(X, y, max_depths, criterion=criterion)



clf = DecisionTreeClassifier(min_samples_split=split)

# your script

Choosing the best state

from bestree import best_state

from sklearn.tree import DecisionTreeClassifier



from sklearn.datasets import make_blobs # To create random X and y data

X, y = make_blobs(n_samples=100, n_features=2, centers=3, random_state=0)



max_depths = 3

criterion = "gini"



state = best_state(X, y, max_depths, criterion=criterion)



clf = DecisionTreeClassifier(random_state=state)

# your script

Help & Support

Communication:

• Mail: [e-mail]

• Website: https://protdos.com

• Discord: https://discord.gg/Xtk4XbwW4Z

• Reddit: https://reddit.com/u/ProtDos

• Twitter: https://twitter.com/ProtDos

Conclusion

Thanks for everybody that supported me (nobody)