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Objective

The challenge is to create a model that uses data from the first 24 hours of intensive care to predict patient survival. MIT's GOSSIS community initiative, with privacy certification from the Harvard Privacy Lab, has provided a dataset of more than 130,000 hospital Intensive Care Unit (ICU) visits from patients, spanning a one-year timeframe. This data is part of a growing global effort and consortium spanning Argentina, Australia, New Zealand, Sri Lanka, Brazil, and more than 200 hospitals in the United States.

Data Description

MIT's GOSSIS community initiative, with privacy certification from the Harvard Privacy Lab, has provided a dataset of more than 130,000 hospital Intensive Care Unit (ICU) visits from patients, spanning a one-year timeframe. This data is part of a growing global effort and consortium spanning Argentina, Australia, New Zealand, Sri Lanka, Brazil, and more than 200 hospitals in the United States.

The data includes:

Training data for 91,713 encounters.
Unlabeled test data for 39,308 encounters, which includes all the information in the training data except for the values for hospital_death.
WiDS Datathon 2020 Dictionary with supplemental information about the data, including the category (e.g., identifier, demographic, vitals), unit of measure, data type (e.g., numeric, binary), description, and examples.
Sample submission files

Ensemble Learning :

A collection of several models working together on a single set is called an Ensemble and the method is called Ensemble Learning.

Ensemble methods combine several trees base algorithms to construct better predictive performance than a single tree base algorithm. The main principle behind the ensemble model is that a group of weak learners come together to form a strong learner, thus increasing the accuracy of the model. When we try to predict the target variable using any machine learning technique, the main causes of difference in actual and predicted values are noise, variance, and bias.

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Pic Credit: medium.com

Voting Classifier :

A Voting Classifier is a machine learning model that trains on an ensemble of numerous models and predicts an output (class) based on their highest probability of chosen class as the output.It simply aggregates the findings of each classifier passed into Voting Classifier and predicts the output class based on the highest majority of voting.

Voting Classifier supports two types of votings.

Hard Voting: In hard voting, the predicted output class is a class with the highest majority of votes i.e the class which had the highest probability of being predicted by each of the classifiers.

Soft Voting: In soft voting, the output class is the prediction based on the average of probability given to that class.