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Logistic Regression: Predicting Yes or No
access_time 2025-04-26T11:57:23.985Z face Nerchuko
Logistic Regression Explained: Predicting Categories Learn how this fundamental algorithm classifies data like 'Yes/No' or 'Spam/Not Spam'. Logistic Regression: Predicting Yes or No Machine learning helps us make predictions. Sometimes we predict numbers (like house prices - that's Regression). Othe...
Is Your Classifier Confused? Understanding the Confusion Matrix
access_time 2025-04-26T11:46:31.69Z face Nerchuko
Confusion Matrix & Classification Metrics Explained Go beyond accuracy! Understand how well your classification model *really* performs. Is Your Classifier Confused? Understanding the Confusion Matrix When we build a model to classify things (like telling spam emails from important ones, or detectin...
Dealing with Imbalanced Data: Building Fairer Models
access_time 2025-04-26T11:29:19.219Z face Nerchuko
Handling Imbalanced Data: Don't Let Your Model Be Biased! Learn Over-sampling and Under-sampling techniques to build fairer ML models. Dealing with Imbalanced Data: Building Fairer Models Imagine you're building a model to detect a rare disease. Most people in your data are healthy (Class 0), and on...
What is Regularization? Keeping Models in Check
access_time 2025-04-26T11:22:33.911Z face Nerchuko
L1 vs L2 Regularization: Taming Complex Models Learn how Lasso (L1) and Ridge (L2) prevent overfitting and improve your models. What is Regularization? Keeping Models in Check Imagine training a machine learning model, like one predicting house prices. If the model gets too complex (maybe using too ...
Backward Elimination: Building Simpler, Smarter Models
access_time 2025-04-26T11:13:00.584Z face Nerchuko
Backward Elimination: Simplifying Your Regression Models Learn how to remove less useful features step-by-step using P-values and Adjusted R². Backward Elimination: Building Simpler, Smarter Models When building a Multiple Linear Regression model, we often start by including many potential input fea...