Plot Decision Boundary for Logistic Regression in Python

The code 'plot_decision_boundary(lr, x_train, y_train)' is a function call to plot the decision boundary of a logistic regression model.

This function takes three arguments:

• 'lr': The logistic regression model object trained on the training data.
• 'x_train': The feature matrix of the training data.
• 'y_train': The target vector of the training data.

The purpose of this function is to visualize how the logistic regression model separates different classes in the feature space. The decision boundary is the line that distinguishes between these classes. Plotting this boundary provides insights into the model's ability to classify data.

The function utilizes the 'matplotlib' library to create a scatter plot of the training data points, using different colors for different classes. It then generates a mesh grid of points covering the entire feature space. For each point in this grid, the logistic regression model predicts the class label. The function subsequently plots the decision boundary by contouring the predicted class labels.

This visualization aids in understanding the model's performance and identifying potential problems like underfitting or overfitting. It also visually represents how the logistic regression model separates distinct classes within the feature space.