Implementing Logistic Regression in Python

In this lesson, we will implement logistic regression using the scikit-learn library. We’ll work with a binary classification dataset.

Step 1: Importing Libraries and Dataset

We’ll start by importing the necessary libraries and loading a dataset. For simplicity, we will use scikit-learn's make_classification to generate a binary classification dataset.

from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

# Generate dataset
X, y = make_classification(n_samples=100, n_features=2, n_classes=2, random_state=42)

# Split into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

Step 2: Creating the Logistic Regression Model

We use scikit-learn’s LogisticRegression class to create and train the model.

model = LogisticRegression()
model.fit(X_train, y_train)

Step 3: Making Predictions

Once the model is trained, we use it to make predictions on the test data.

y_pred = model.predict(X_test)

Step 4: Evaluating the Model

We evaluate the performance of the model using accuracy as the metric.

accuracy = accuracy_score(y_test, y_pred)
print(f"Model Accuracy: {accuracy:.2f}")

Step 5: Visualizing Decision Boundaries

To better understand the model’s performance, we visualize the decision boundaries.

import matplotlib.pyplot as plt
import numpy as np

# Define grid for plotting
def plot_decision_boundary(X, y, model):
    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
    xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1), np.arange(y_min, y_max, 0.1))

    Z = model.predict(np.c_[xx.ravel(), yy.ravel()])
    Z = Z.reshape(xx.shape)
    
    plt.contourf(xx, yy, Z, alpha=0.8)
    plt.scatter(X[:, 0], X[:, 1], c=y, edgecolor='k')
    plt.title('Decision Boundary')
    plt.show()

plot_decision_boundary(X, y, model)

Step 6: Tuning the Model

We can improve model performance by adjusting hyperparameters like C, which controls regularization strength.

model = LogisticRegression(C=0.5)
model.fit(X_train, y_train)

Challenges in Logistic Regression

Challenges include:

• Handling imbalanced datasets.
• Dealing with highly correlated features.
• Ensuring sufficient training data for effective predictions.

Summary and Next Steps

In this lesson, we:

• Implemented logistic regression using scikit-learn.
• Made predictions and evaluated the model.
• Visualized decision boundaries.

Next, we will explore how to evaluate model performance using metrics like F1 score, precision, and recall.