Fatima Jannetmahia.hashnode.dev·Nov 27, 2024ML chapter 10: Model Selection & BoostingWelcome to the final part of our journey. After we built our Machine Learning models, I guess we still have some questions: How do we handle the bias-variance tradeoff when building a model and checking its performance? How do we pick the best valu...10 likes·29 readsMachine Learning (Python)Xgboost
Emeron Marcelleemerondomain.hashnode.dev·Sep 22, 2024Model Selection in Machine Learning: GridSearchCV, RandomizedSearchCV, and TPOTIn machine learning, selecting the right model and tuning its hyperparameters is a critical step toward achieving optimal performance. Several techniques exist to assist in this process, ranging from exhaustive searches like GridSearchCV to more rand...randomsearchcv
Ojo Timilehincampeone.hashnode.dev·Sep 12, 2024AUC-ROC Curve: A Comprehensive Guide to Model Selection in Machine LearningIntroduction There are a few Machine Learning (ML) algorithms to choose from when building an ML model. Different ML algorithms perform differently on the same dataset due to differences in algorithm complexity, bias-variance tradeoff, nature of data...AUCROCCurve
Md Shahriyar Al Mustakim Mitulmitul-shahriyar.hashnode.dev·Aug 8, 2024Machine Learning : Model Selection Techniques, XGBoost (Part 34)Till now, we have been dividing our main dataset to Training Set and Test set Let's now split the Training set to 10 parts Then what we're going to do is we're going to train the data on nine of these folds and keep one fold as an unseen fold for v...ML From scratch to ExpertML Model selection
