Mle, Machine Learning Models, Maximum Likelihood, Logistic Regression, Practical Mle.
intercept to features result = minimize(neg_log_likelihood, initial_theta, args=(X_train_with_intercept, y_train), method='BFGS') estimated_theta = result.x # Plot the logistic regression curve x_values = np.linspace(min(X_train), max(X_train), 100).reshape(-1, 1) x_values_with_intercept =