Logistic regression is a popular machine learning algorithm used for binary classification problems. In this tutorial, we'll dive deep into the details of logistic regression, covering its fundamentals, implementation, and practical examples.

Key Concepts

  • Binary Classification: Logistic regression is designed for binary classification problems, where the target variable can take only two values (e.g., 0 and 1, yes and no).
  • Sigmoid Function: The sigmoid function is the cornerstone of logistic regression, which maps the input values to the range between 0 and 1.
  • Cost Function: The cost function in logistic regression is the logistic loss, which measures the difference between the predicted probabilities and the actual binary labels.

Implementation

Here's a simple example of logistic regression using Python's scikit-learn library:

from sklearn.linear_model import LogisticRegression
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

# Load the Iris dataset
iris = load_iris()
X = iris.data
y = iris.target

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Create and train the logistic regression model
model = LogisticRegression()
model.fit(X_train, y_train)

# Evaluate the model on the test set
accuracy = model.score(X_test, y_test)
print(f"Accuracy: {accuracy}")

Further Reading

For more information on logistic regression, you can refer to the following resources:

Logistic Regression Diagram