Multiple Model Ensembling
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Updated
Apr 4, 2017 - Python
Multiple Model Ensembling
Predictive Machine Learning Project
Dataton URJC 2017
How to build classification models using H2O in R
Udacity Machine Learning Project 3(Python)
competitions launched on website AnalyticsVidhya.com
Different classification algorithms are used to predict income levels of potential donors. The hyperparameters of the classifiers are optimized via grid_search and, among others, an ensemble classifier with the highest prediction performance is selected for this classification project.
Create an arbitrary graph of models and meta-models to form an ensemble. This can be viewed as a generalisation of stacking ensembles.
Statoil/C-CORE Iceberg Classifier Challenge's codes
Reduce the model complexity by 612 times, and memory footprint by 19.5 times compared to base model, while achieving worst case accuracy threshold.
This repository contains the model that I developed in the Toxic Comment Classification Challenge hosted by kaggle.
Deep Learning and Decision Trees Ensemble Methods based Audiovisual Perceived Quality Models. These models are based on the INRS audiovisual quality dataset that can be found on this GitHub repository.
This is a weighted blending machine implemented using a neural network. The advantage of using a neural network is that the weights assigned to the models for the final result is assigned by the neural network based on backpropagation.
Archives for the tutorial article Tutorial: Increasing the Predictive Power of Your Machine Learning Models with Stacking Ensembles
A Repo of various End-to-End analysis in statistical approach, exploratory data analysis (EDA) , feature engineering and modelling.
Short experiments on the Mixture of Softmax technique
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