R/cvma: Cross-validation-based maximal associations
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Updated
Nov 9, 2017 - R
R/cvma: Cross-validation-based maximal associations
Create an arbitrary graph of models and meta-models to form an ensemble. This can be viewed as a generalisation of stacking ensembles.
Predict respiratory patient mortality in ICU units using the MIMIC III database
A web-based application for quick, scalable, and automated hyperparameter tuning and stacked ensembling in Python.
Implementation of Super Learner classifier and comparison with Logistic regression, SVC and Random Forests classifier.
This project intends to solve the house hunt problem by sending the updates of new listings as per the selection criteria of the user by filtering spam in housing listings using NLP. It uses SMTP to send emails, nltk for NLP and tkinter for creating UI
Project for Fundamentals of Data Science. Forked from https://github.com/luigiberducci/FDS-Project-HousePrices
Cross-validation-based maximal associations
This my entry for the Titanic competition on Kaggle. May 2019: public score is 0.80382, which is a top 10% ranking on the leader board of around 11.249 participants.
This repository contains the approach that led us to win the MLDS Republic Day Hackathon.
This repository contains my implementation for Energy Disaggregation of appliances from mains consumption using stacked ensemble deep learning
In class Kaggle competition on predicting bankruptcy of a firm
Unbalanced data classification
Intuitive Package for Heterogeneous Ensemble Meta-Learning (Classification, Regression) that is fully-automated
Geolocating twitter users by the content of their tweets
My own stacked CNN model architecture for CIFER10 data classification
Python code for stacking models, includes extracting model probabilities and assessing misclassified cases
Utilizing Machine Learning for portfolio selection with the aim of out-performing benchmark indices
CoMoMo combines multiple mortality forecasts using different model combinations. See more from the paper here https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3823511
Detecting Fraudulent Blockchain Accounts on Ethereum with Supervised Machine Learning
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