Shashikala Gupta·4d agoTitanic — Machine Learning from Disaster: My First Steps into Kaggle CompetitionsMachine Learning becomes much more interesting when we move beyond theoretical concepts and start applying them to real-world datasets.
Jema Dacoco·Nov 2Modern Data Mining Paradigms: Ensemble Learning and Association Rule AnalysisA Comprehensive Technical Guide on Random Forests, Boosting, and Apriori Algorithms
Wijitra Sing·Oct 31Exploratory of EEG Markers for Pleasantness Olfactory StimuliIn the fields of product development and sensory science, olfactory pleasantness heavily influences consumer emotions and purchasing…A response icon1A response icon1
InThe GravitybySoundarya Joshi·Oct 25The urge to get lost in the woodsno vehicle, no compassA response icon1A response icon1
Akshat Gupta·Oct 23Decision Trees Vs Random Forest Vs Gradient BoostingThese three are very important and useful Ensemble methods
Vinaitheerthan Renganathan·Oct 23𝗪𝗵𝘆 𝗥𝗮𝗻𝗱𝗼𝗺 𝗙𝗼𝗿𝗲𝘀𝘁𝘀 𝗗𝗼𝗻’𝘁 𝗢𝘃𝗲𝗿𝗳𝗶𝘁 𝗮𝘀 𝗧𝗿𝗲𝗲𝘀 𝗚𝗿𝗼𝘄…𝘈 𝘮𝘢𝘵𝘩𝘦𝘮𝘢𝘵𝘪𝘤𝘢𝘭 𝘢𝘯𝘥 𝘱𝘳𝘢𝘤𝘵𝘪𝘤𝘢𝘭 𝘨𝘶𝘪𝘥𝘦 𝘵𝘰 𝘓𝘦𝘰 𝘉𝘳𝘦𝘪𝘮𝘢𝘯’𝘴 𝘚𝘵𝘳𝘰𝘯𝘨 𝘓𝘢𝘸 𝘰𝘧 𝘓𝘢𝘳𝘨𝘦…
Karthick Raja Mohan·Oct 22A crowd of bad trees makes one good guessRandom forests, explained without the noise. Machine learning, simply · 07
Frankline Sable·Oct 13When 85% Accuracy Isn’t Good Enough: Understanding Machine Learning Beyond the NumbersExploring predictive modelling, data preparation, Random Forests, and why understanding your data matters more than chasing impressive…
Ayşe Simal Alpözer·Oct 12Why Does My Model Think a Random Column Matters?A beginner-friendly Python experiment with Random Forest, overfitting, and permutation importance.
Rayan Yassminh·Oct 11ML Ensemble Methods: Bagging Builds a Committee, Boosting Builds a Revision HistoryOne builds a committee. The other builds a revision history. A friendly, in-depth guide to what these ensemble methods actually do and how…
