# Ensemble Models

# Ensemble Models#

This section describes experiments using ensemble models that combine the probability outputs from logistic regression, random forests, and neural net models, and use those either as the sole inputs for a new model, or alongside the normal features in the training and test set.

This section contains the following notebooks (all experiments are performed with a single training/test split):

*Ensemble model fitting - generate probabilities*: Generates the prediction probabilities from logistic regression, random forests, and a neural network.*Comparison of model outputs*Compares the outputs of three types of model.*Ensemble combine with logistic regression*: combine output from logistic regression, random forests, and neural network models in a single logistic regression model, with or without original data features.*Ensemble combine with random forests*: combine output from logistic regression, random forests, and neural network models in a single random forests model, with or without original data features.*Ensemble combine with neural network*: combine output from logistic regression, random forests, and neural network models in a single neural network model, with or without original data features.*Summary of results from ensemble models*: summary of ensemble model accuracies.