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Support Vector Machines (SVM): Finding the Best Divider
access_time 2025-04-26T12:23:43.303Z face Nerchuko
Support Vector Machines (SVM) Explained Mastering the art of finding the optimal boundary between classes. Support Vector Machines (SVM): Finding the Best Divider Imagine you have a scatter plot with two different groups of dots (say, blue and green). How would you draw a line to separate them? You ...
K-Nearest Neighbors (KNN): Learning by Similarity
access_time 2025-04-26T12:13:59.993Z face Nerchuko
K-Nearest Neighbors (KNN) Explained Simply Understand one of the simplest yet powerful classification algorithms. K-Nearest Neighbors (KNN): Learning by Similarity Imagine you meet someone new and want to guess if they like action movies or comedies. What might you do? You could look at their closes...
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...