--- title: "#regularization articles | Hashnode" description: "Explore 22 articles tagged #regularization on Hashnode. Tutorials, guides, and how-tos from the community." url: "https://hashnode.com/tag/regularization" --- Tag # #regularization 22 posts · 2 followers [Henry Ha](https://hashnode.com/@henryha) · [Apr 15](https://hashnode.com/posts/cross-validation-pipelines-hyperparameter-tuning-with-scikit-learn/69df6997e41a62132cb65c2d) ### [Cross-Validation, Pipelines & Hyperparameter Tuning with Scikit-learn](https://databeginnersfriendlytutorials.hashnode.dev/cross-validation-pipelines-hyperparameter-tuning-with-scikit-learn?utm_source=hashnode&utm_medium=feed) When building a machine learning model, it's tempting to train it on your data, check the score, and call it done. But that score is misleading — the model has already "seen" the data it's being evalu 26 min read · [databeginnersfriendlytutorials.hashnode.dev](https://databeginnersfriendlytutorials.hashnode.dev/cross-validation-pipelines-hyperparameter-tuning-with-scikit-learn?utm_source=hashnode&utm_medium=feed) [Prathmesh Kushwaha](https://hashnode.com/@PrathmeshKush) · [Feb 5](https://hashnode.com/posts/regularization-in-machine-learning-what-i-learned-by-implementing-it-from-scratch/6984d65329e816f29297ff5e) ### [Regularization in Machine Learning: What I Learned by Implementing It From Scratch](https://prathmeshkush.hashnode.dev/regularization-in-machine-learning-what-i-learned-by-implementing-it-from-scratch?utm_source=hashnode&utm_medium=feed) Implement L1, L2, and Elastic Net to understand regularization's impact on overfitting and feature selection in machine learning models 16 min read · [prathmeshkush.hashnode.dev](https://prathmeshkush.hashnode.dev/regularization-in-machine-learning-what-i-learned-by-implementing-it-from-scratch?utm_source=hashnode&utm_medium=feed) [Nidhi Bhushan](https://hashnode.com/@Nidhii06) · [Feb 4](https://hashnode.com/posts/i-thought-l1-lasso-turns-out-i-was-half-right/6983a5a311053fb23d05652d) ### [I Thought L1 = Lasso. Turns Out I Was Half Right](https://mlbynidhi.hashnode.dev/i-thought-l1-lasso-turns-out-i-was-half-right?utm_source=hashnode&utm_medium=feed) How a small terminology confusion taught me the real meaning of regularization in ML 4 min read · [mlbynidhi.hashnode.dev](https://mlbynidhi.hashnode.dev/i-thought-l1-lasso-turns-out-i-was-half-right?utm_source=hashnode&utm_medium=feed) [Mohammed Haris Ammar](https://hashnode.com/@harisammar) · [Jan 22](https://hashnode.com/posts/understanding-supervised-learning-a-step-by-step-guide/6971eca1f50282ede072f491) ### [Understanding Supervised Learning: A Step-by-Step Guide](https://ammar117blogs.hashnode.dev/understanding-supervised-learning-a-step-by-step-guide?utm_source=hashnode&utm_medium=feed) What is supervised machine learning? This is one of the main models in the machine learning module and is one of the most basic and widely used training models. In this model, you train the system by providing the correct features and the correct ans... 7 min read · [ammar117blogs.hashnode.dev](https://ammar117blogs.hashnode.dev/understanding-supervised-learning-a-step-by-step-guide?utm_source=hashnode&utm_medium=feed) [Paul Omagbemi](https://hashnode.com/@Omagbs) · [Feb 18, 2025](https://hashnode.com/posts/regularization-in-logistic-regression-same-idea-different-function/67b51bdb8a04b2d94d04e8ee) ### [Regularization in Logistic Regression: Same Idea, Different Function](https://omagbs.hashnode.dev/regularization-in-logistic-regression-same-idea-different-function?utm_source=hashnode&utm_medium=feed) Well, still similar to the Regularization applied to linear, the regularized cost function for logistic regression is defined by: $$J(\\mathbf{w}, b) = -\\frac{1}{m} \\sum\_{i=1}^m \\left\[ y^{(i)} \\log(f\_{\\mathbf{w}, b}(\\mathbf{x}^{(i)})) + (1 - y^{(i)}) ... 1 min read · [omagbs.hashnode.dev](https://omagbs.hashnode.dev/regularization-in-logistic-regression-same-idea-different-function?utm_source=hashnode&utm_medium=feed) [Paul Omagbemi](https://hashnode.com/@Omagbs) · [Feb 18, 2025](https://hashnode.com/posts/the-balancing-act-choosing-the-right-lambda-in-regularization-linear-regression/67b4bcbbc28336dd5b951e8a) ### [The Balancing Act: Choosing the Right Lambda in Regularization (Linear Regression)](https://omagbs.hashnode.dev/the-balancing-act-choosing-the-right-lambda-in-regularization-linear-regression?utm_source=hashnode&utm_medium=feed) Since we can't be sure which of the parameter to penalize, we penalize all by adding the Regularization term to the cost function. A regularized cost function is defined by: $$J(\\mathbf{w}, b) = \\frac{1}{2m} \\sum\_{i=1}^m \\left( f\_{\\mathbf{w}, b}(\\mat... 1 min read · [omagbs.hashnode.dev](https://omagbs.hashnode.dev/the-balancing-act-choosing-the-right-lambda-in-regularization-linear-regression?utm_source=hashnode&utm_medium=feed) [Paul Omagbemi](https://hashnode.com/@Omagbs) · [Feb 17, 2025](https://hashnode.com/posts/shrink-the-weights-save-the-model-a-quick-look-at-regularization/67b3b48c9e8d6b985f2b4bbe) ### [Shrink the Weights, Save the Model: A Quick Look at Regularization](https://omagbs.hashnode.dev/shrink-the-weights-save-the-model-a-quick-look-at-regularization?utm_source=hashnode&utm_medium=feed) So, to simply put, Regularization is just simply making your parameters smaller and relatively less effective to avoid overfitting. oh, and the beautiful thing? — you get to keep all your features because you don't know which one might just make the ... 1 min read · [omagbs.hashnode.dev](https://omagbs.hashnode.dev/shrink-the-weights-save-the-model-a-quick-look-at-regularization?utm_source=hashnode&utm_medium=feed) [Omkar Kasture](https://hashnode.com/@omkar2483) · [Feb 4, 2025](https://hashnode.com/posts/model-evaluation-and-validation-techniques/67a2471beb5871004e8f11b8) ### [Model Evaluation and Validation Techniques](https://omkarkasture.hashnode.dev/model-evaluation-and-validation-techniques?utm_source=hashnode&utm_medium=feed) Throughout this series, we’ve explored various ML models and their evaluation techniques. In this blog, let’s summarize key metrics. Evaluating Machine Learning Models In the world of machine learning, we want to teach computers to make predictions, ... 9 min read · [omkarkasture.hashnode.dev](https://omkarkasture.hashnode.dev/model-evaluation-and-validation-techniques?utm_source=hashnode&utm_medium=feed) [Fadhil Elrizanda](https://hashnode.com/@fadhilelrizanda) · [Dec 27, 2024](https://hashnode.com/posts/regularization-techniques-to-improve-deep-learning-performance/676f338c1dfd63711bd6e823) ### [Regularization Techniques to Improve Deep Learning Performance](https://elrizanda.hashnode.dev/regularization-techniques-to-improve-deep-learning-performance?utm_source=hashnode&utm_medium=feed) Overview When building and training deep learning models we often facing the problem of overfitting. Overfitting may happen due the model complexity or the model is “memorized” the training dataset instead of learning generalizable patterns. This be... 5 min read · [elrizanda.hashnode.dev](https://elrizanda.hashnode.dev/regularization-techniques-to-improve-deep-learning-performance?utm_source=hashnode&utm_medium=feed) [Bitingo Josaphat](https://hashnode.com/@BitingoJosaphat) · [Jun 8, 2024](https://hashnode.com/posts/beat-overfitting-essential-regularization-techniques-for-machine-learning/6664cb24febf14d271740d06) ### [Beat Overfitting: Essential Regularization Techniques for Machine Learning](https://bitingo-the-deep-neural-nets.hashnode.dev/beat-overfitting-essential-regularization-techniques-for-machine-learning?utm_source=hashnode&utm_medium=feed) A Guide on Conquering Models Overfitting with Regularization in Machine Learning 5 min read · [bitingo-the-deep-neural-nets.hashnode.dev](https://bitingo-the-deep-neural-nets.hashnode.dev/beat-overfitting-essential-regularization-techniques-for-machine-learning?utm_source=hashnode&utm_medium=feed) [4](https://hashnode.com/posts/beat-overfitting-essential-regularization-techniques-for-machine-learning/6664cb24febf14d271740d06)