logregnumpy 0.1.1

Logistic Regression Classifier

logregnumpy

Pet Project. Logistic Regressor Classifier.

Performs a gradient descent method for a loss minimizing.

Works with binary and multiclass targets.

Parameters

lr : float, default=1e-3 Learning rate (size) for each step of an gradient descent.

l2_reg : float, default=0.2 Degree of L2 penalty.

epochs : int, default=100 Number of gradient descent iterations.

Examples

>>> from sklearn.datasets import load_iris  
>>> from logregnumpy import LogRegNumpy  
>>> X, y = load_iris(return_X_y=True)  
>>> model = LogRegNumpy(l2_reg=0.1, epochs=1000)  
>>> model.fit(X, y)  
>>> model.predict(X)[:3]  

array([0, 0, 0])  

>>> model.predict_proba(X)[:3]

array([[9.69584306e-01, 3.04018742e-02, 1.38198704e-05],  
       [9.32753885e-01, 6.71844981e-02, 6.16165599e-05],  
       [9.57931295e-01, 4.20313028e-02, 3.74027136e-05]])  

Methods

fit(X, y, verbose=False, plot=False) Fit the model according to the given training data. May return a loss value graph.

Parameters

X : array-like of shape (n_samples, n_features) Training vector, where n_samples is the number of samples and
n_features is the number of features.
y : array-like of shape (n_samples,)
Target vector relative to X.
verbose : bool, default=False
If true, returns array with loss values on each iteration.
plot : bool, default=False
If true, returns a loss value graph.

predict(X) Predict class labels for samples in X.
predict_proba(X) Probability estimates.

Notes

To successfully uninstall the package from Jupyter notebook, use the following code:

pip uninstall logregnumpy --yes