Implementation of KNN using PySpark. The KNN was used on two separate datasets (https://archive.ics.uci.edu/ml/datasets/iris and https://archive.ics.uci.edu/ml/datasets/Fertility). The data was first normalized, also using PySpark. Euclidean Distance was used as the similarity measure. The optimal k found for both datasets was 5. The iris dataset had a test accuracy of 97% and the fertility dataset had a test accuracy of 88%.
CobraML: Completely Customizable What is it? CobraML is a python library built on both numpy and numba. Unlike other ML libraries CobraML gives the us
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Skill-vs-chance-games-analysis - Finding a method to objectively quantify skill versus chance in games, using reinforcement learning
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End of maintenance for GPyOpt Dear GPyOpt community! We would like to acknowledge the obvious. The core team of GPyOpt has moved on, and over the past
Dive into Machine Learning Hi there! You might find this guide helpful if: You know Python or you're learning it 🐍 You're new to Machine Learning You
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Learn Machine Learning Algorithms by doing projects in Python and R Programming Language. This repo covers all aspect of Machine Learning Algorithms.
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Kaggle Tweet Sentiment Extraction Competition: 1st place solution (Dark of the Moon team)
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Object Relation Transformer This is a PyTorch implementation of the Object Relation Transformer published in NeurIPS 2019. You can find the paper here
DAS Dual Adaptive Sampling for Machine Learning Interatomic potential. How to cite If you use this code in your research, please cite this using: Hong
Unsupervised-Machine-Learning Flat Clustering K-Means clusternig example with Python and Scikit-learn Flat clustering Clustering algorithms group a se
UpliftML is a Python package for scalable unconstrained and constrained uplift modeling from experimental data. To accommodate working with big data, the package uses PySpark and H2O models as base l
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