logistic-regression

Logistic Regression

Logistic Regression is a statistical method for binary classification problems, where the goal is to predict one of two outcomes based on a set of input features.

Unlike linear regression, logistic regression models the relationship between the inputs and the binary output as a logistic function, resulting in a probability that can be thresholded to make a binary prediction.

source

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📍 The full implementation is available here!

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Demo

Import sklearn in order to create a random dataset and to compare our performance with the pre-implemented one.

from sklearn.datasets import make_classification
from sklearn.linear_model import LogisticRegression as LR
from sklearn.metrics import accuracy_score

Create the dataset via make_classification.

X, y = make_classification(
        n_samples=1000, 
        n_features=10, 
        n_informative = 7,
        n_classes = 2, 
        random_state=42 
    )

X_train, y_train = X[:700], y[:700]
X_test, y_test = X[700:], y[700:]

Evaluate our model.

lr = LogisticRegression()
lr.fit(X_train,y_train)
y_pred = lr.predict(X_test)
print(f'Accuracy (M) = {accuracy_score(y_pred,y_test):.2f}')

Accuracy (M) = 0.73

Evaluate the implementation available in scikit-learn:

lr_scikit = LR()
lr_scikit.fit(X_train,y_train)
y_pred = lr.predict(X_test)
print(f'Accuracy (S) = {accuracy_score(y_pred,y_test):.2f}')

Accuracy (S) = 0.73