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HHHenry Hadatabeginnersfriendlytutorials.hashnode.devApr 15 · 26 min read

Cross-Validation, Pipelines & Hyperparameter Tuning with Scikit-learn

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

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PKPrathmesh Kushwahaprathmeshkush.hashnode.devFeb 5 · 16 min read

Regularization in Machine Learning: What I Learned by Implementing It From Scratch

Introduction: Why I decided to implement Regularization From Scratch After implementing Linear machine learning models. I learned about overfitting, bias-variance tradeoff, and regularization in theory. L1 and L2 penalties existed, and I could explai...

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NBNidhi Bhushanmlbynidhi.hashnode.devFeb 4 · 4 min read

I Thought L1 = Lasso. Turns Out I Was Half Right

Introduction While working on an Employee Turnover Prediction project, I ran into a confusion that almost every Machine Learning beginner faces: Is L1 the same as Lasso?If yes, why did my classification model break when I applied Lasso?And why does ...

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MHMohammed Haris Ammarammar117blogs.hashnode.devJan 22 · 7 min read

Understanding Supervised Learning: A Step-by-Step Guide

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...

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POPaul Omagbemiomagbs.hashnode.devFeb 18, 2025 · 1 min read

Regularization in Logistic Regression: Same Idea, Different Function

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)}) ...

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POPaul Omagbemiomagbs.hashnode.devFeb 18, 2025 · 1 min read

The Balancing Act: Choosing the Right Lambda in Regularization (Linear Regression)

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...

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POPaul Omagbemiomagbs.hashnode.devFeb 17, 2025 · 1 min read

Shrink the Weights, Save the Model: A Quick Look at Regularization

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 ...

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OKOmkar Kastureomkarkasture.hashnode.devFeb 4, 2025 · 9 min read

Model Evaluation and Validation Techniques

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, ...

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FEFadhil Elrizandaelrizanda.hashnode.devDec 27, 2024 · 5 min read

Regularization Techniques to Improve Deep Learning Performance

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...

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BJBitingo Josaphatbitingo-the-deep-neural-nets.hashnode.devJun 8, 2024 · 5 min read

Beat Overfitting: Essential Regularization Techniques for Machine Learning

In machine learning, overfitting is a common problem where models perform well on training data but fail to generalize to unseen data. This article explores essential regularization techniques that help combat overfitting and improve model performanc...