Pet Project. Logistic Regressor Classifier.
Performs a gradient descent method for a loss minimizing.
Works with binary and multiclass targets.
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.
>>> 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]])
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.
To successfully uninstall the package from Jupyter notebook, use the following code:
pip uninstall logregnumpy --yes