A deep convolutional network made of stacked feature extractors
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Updated
May 5, 2020 - Python
A deep convolutional network made of stacked feature extractors
Identify the type of disease present on a Cassava Leaf image
final_project of sun_ai_courses
This repository contains an example of each of the Ensemble Learning methods: Stacking, Blending, and Voting. The examples for Stacking and Blending were made from scratch, the example for Voting was using the scikit-learn utility.
Learning with Subset Stacking
A Stacking-Based Model for Non-Invasive Detection of Coronary Heart Disease
Kaggle Competition
Machine Learning Model to predict student graduation grade
Trabajos prΓ‘cticos realizados en la materia OrganizaciΓ³n de Datos de la FIUBA.
State-of-the art Automated Machine Learning python library for Tabular Data
This streamlit app predicts the churn rate using Gradient Boosting models (XGBoost, Catboost, LightGBM) on IBM Customer Churn Dataset
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This project is dedicated to accurately classify Alzheimer's disease into Demented, Non-demented and Converted Category.
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