tisthemachinelearner 0.8.4

Lightweight interface to scikit-learn, xgboost, lightgbm, catboost, with 2 classes

tisthemachinelearner

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Lightweight interface to scikit-learn with 2 classes, Classifier and Regressor. Home of FiniteDiffRegressor (see Backpropagating quasi-randomized neural networks https://thierrymoudiki.github.io/blog/2025/06/23/python/backprop-qrnn).

Installing (for Python and R)

Python

• 1st method: by using pip at the command line for the stable version

pip install tisthemachinelearner

• 2nd method: from Github, for the development version

pip install git+https://github.com/Techtonique/tisthemachinelearner.git

or

git clone https://github.com/Techtonique/tisthemachinelearner.git
cd tisthemachinelearner
make install

Examples

import numpy as np
from sklearn.datasets import load_diabetes, load_breast_cancer
from sklearn.model_selection import train_test_split
from tisthemachinelearner import Classifier, Regressor

# Classification
X, y = load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

clf = Classifier("LogisticRegression", random_state=42)
clf.fit(X_train, y_train)
print(clf.predict(X_test))
print(clf.score(X_test, y_test))

clf = Classifier("RandomForestClassifier", n_estimators=100, random_state=42)
clf.fit(X_train, y_train)
print(clf.predict(X_test))
print(clf.score(X_test, y_test))

# Regression
X, y = load_diabetes(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

reg = Regressor("LinearRegression")
reg.fit(X_train, y_train)
print(reg.predict(X_test))
print(np.sqrt(np.mean((reg.predict(X_test) - y_test) ** 2)))

reg = Regressor("RidgeCV", alphas=[0.01, 0.1, 1, 10])
reg.fit(X_train, y_train)
print(reg.predict(X_test))
print(np.sqrt(np.mean((reg.predict(X_test) - y_test) ** 2)))

License

BSD 3-Clause © T. Moudiki, 2025.