Finding the best parameters for any algorithm
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Updated
Nov 1, 2017 - Jupyter Notebook
Finding the best parameters for any algorithm
A jupyter notebook for binary classification of breast cancer using XGBoost with Bayesian optimization.
Implementation, analysis and benchmarking of optimization algorithms. Developed in Python and results showed in Jupyter Notebook
Concepts of Bayesian Statistics, Bayesian inference, computational techniques and knowledge about the different types of models as well as model selection procedures.
Bayesian Optimization for hyperparameter tuning in machine learning using a Jupyter Notebook. This repository demonstrates optimizing a Gradient Boosting Classifier with practical examples and clear explanations.
Notebooks and code snippets demonstrating machine learning techniques.
This notebook demonstrates timeseries classification for crop identification on a subset of the MiniTimeMatch dataset by training an LSTM model.
Python Scripts and Jupyter Notebooks
Notebooks about Bayesian methods for machine learning
This Jupyter Notebook implements Bayesian modeling techniques to fit a posterior distribution and forecast demand for an e-commerce company.
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