[SIGE-MII-UGR-2016-17] Competición en Kaggle: Titanic
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Updated
Apr 28, 2017 - R
[SIGE-MII-UGR-2016-17] Competición en Kaggle: Titanic
How to build classification models using H2O in R
Showcase for using H2O and R for churn prediction (inspired by ZhouFang928 examples)
R package for automatic hyper parameter tuning and ensembles with deep learning, gradient boosting machines, and random forests. Powered by h2o.
This project compares multiple bagging and boosting methods for anomaly detection for the Gecco challenge.
Material from "Random Forests and Gradient Boosting Machines in R" presented at Machine Learning Day '18
Loan Default Prediction, Individual Level Loan Data, Machine Learning, Logistic regression, Ridge, LASSO, Gradient Boosting, SVM, Random Forest
In this project we can see in action and in detail a big part of the ML pipeline (data wrangling,model building, model evaluation) that comprises different algorithms and approaches such as Decision Trees (RPART), Linear Discriminant Analysis (LDA), Gradient Boosting Machne (GBM), Random Forest (RF) Support Vector Machine (SVM) with or without M…
🏙 What's an appropriate price? Predicting Milan's apartment prices.
A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).
mlim: single and multiple imputation with automated machine learning
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