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Foundations of Non-Parametric Models: KNN, Decision Tree & the Road to Ensembles
This blog explores the foundations of non-parametric models in machine learning, with a special focus on decision trees — their structure, splitting criteria (Gini, entropy), and practical strengths. Learn how decision trees compare with K-Nearest Neighbors (KNN), when to use them, and why they serve as the core building blocks of ensemble methods like Random Forests and Gradient Boosting.
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Optimization Techniques in Regression Tasks
Learn how to solve regression problems efficiently using the right optimization techniques. This guide covers closed-form solutions, gradient-based methods, coordinate descent, second-order solvers, and advanced approaches like proximal algorithms and Bayesian optimization. Ideal for data scientists and ML engineers, this post explains when and why to use different solvers based on dataset size, sparsity, regularization, and computational constraints. Includes Python code, practical tips, and use-case-driven guidance for building scalable, accurate regression models.
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Advanced Parametric Regression Techniques
Explore advanced parametric regression techniques including Bayesian Linear Regression, ARD, MCMC, and Multi-Task Regression. Learn how to model uncertainty, enforce sparsity, and handle multiple outputs in predictive modeling. With Python code, mathematical intuition, and diagnostics, this guide empowers data scientists to build robust, interpretable, and uncertainty-aware regression models for real-world applications in healthcare, finance, and beyond.
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Capturing Non-Linearity in Regression
Master non-linear regression to model complex data relationships in this comprehensive guide. Discover polynomial regression, basis expansions (splines, Fourier, RBF), and non-parametric methods like kernel smoothing and LOESS, with practical Python code, visualizations, and diagnostics. Learn how to avoid overfitting, apply regularization, and engineer features for robust predictions in domains like energy forecasting, financial trends, and biomedical research. Perfect for data scientists and analysts seeking to enhance predictive modeling skills.
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Robust Regression for Noisy Data
Explore robust regression methods—Huber Regression, RANSAC, Theil–Sen Estimator, and Quantile Regression—to handle outliers, heteroscedasticity, and violations of OLS assumptions. Includes mathematical intuition, model behavior, and diagnostic tools.