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Arpin ensemble move #82

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Nov 27, 2017
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Arpin ensemble move #82

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41d0043
Moved: ensemble modeling to applications
djarpin Nov 27, 2017
c7acca5
Updated: README to include ensembling
djarpin Nov 27, 2017
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1 change: 1 addition & 0 deletions README.md
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Expand Up @@ -12,6 +12,7 @@ These examples provide a gentle introduction to machine learning concepts as the
- [Predicting Customer Churn](introduction_to_applying_machine_learning/xgboost_customer_churn) uses customer interaction and service usage data to find those most likely to churn, and then walks through the cost/benefit trade-offs of providing retention incentives. This uses Amazon SageMaker's implementation of [XGBoost](https://github.com/dmlc/xgboost) to create a highly predictive model.
- [Time-series Forecasting](introduction_to_applying_machine_learning/linear_time_series_forecast) generates a forecast for topline product demand using Amazon SageMaker's Linear Learner algorithm.
- [Cancer Prediction](introduction_to_applying_machine_learning/breast_cancer_prediction) predicts Breast Cancer based on features derived from images, using SageMaker's Linear Learner.
- [Ensembling](introduction_to_applying_machine_learning/ensemble_modeling) predicts income using two Amazon SageMaker models to show the advantages in ensembling.

### Introduction to Amazon Algorithms

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0 ...odeling/EnsembleLearnerCensusIncome.ipynb → ...odeling/EnsembleLearnerCensusIncome.ipynb
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0 ...r_development/ensemble_modeling/README.md → ...hine_learning/ensemble_modeling/README.md
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