Feb 5 · 16 min read · 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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Feb 4 · 4 min read · 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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Jan 22 · 7 min read · 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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Feb 18, 2025 · 1 min read · Well, still similar to the Regularization applied to linear, the regularized cost function for logistic regression is defined by: $$J(\mathbf{w}, b) = -\frac{1}{m} \sum_{i=1}^m \left[ y^{(i)} \log(f_{\mathbf{w}, b}(\mathbf{x}^{(i)})) + (1 - y^{(i)}) ...
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Feb 18, 2025 · 1 min read · Since we can't be sure which of the parameter to penalize, we penalize all by adding the Regularization term to the cost function. A regularized cost function is defined by: $$J(\mathbf{w}, b) = \frac{1}{2m} \sum_{i=1}^m \left( f_{\mathbf{w}, b}(\mat...
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Feb 17, 2025 · 1 min read · 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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Feb 4, 2025 · 9 min read · 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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Dec 27, 2024 · 5 min read · 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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Jun 8, 2024 · 5 min read · 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...
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Apr 23, 2024 · 11 min read · Have you ever trained a machine learning model that performed exceptionally on your training data but failed miserably on real-world, unseen data? If so, you've encountered overfitting, a common pitfall in machine learning that regularization techniq...
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