A New, Interactive Approach to Learning Python
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Updated
Nov 8, 2023 - Jupyter Notebook
A New, Interactive Approach to Learning Python
Julia implementation of Decision Tree (CART) and Random Forest algorithms
NeuroData's package for exploring and using progressive learning algorithms
Analytics labs notebooks for Statistics and Business School students
miceRanger: Fast Imputation with Random Forests in R
Conceptual & empirical comparisons between decision forests & deep networks
Become a proficient, productive and powerful programmer with Python
My most frequently used learning-to-rank algorithms ported to rust for efficiency. Try it: "pip install fastrank".
Scripts, tools and example data for mapping wetland ecosystems using data driven R statistical methods like Random Forests and open source GIS
A model combining Deep Neural Networks and (Stochastic) Random Forests.
Exploring QSAR Models for Activity-Cliff Prediction
Using / reproducing DAC from the paper "Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees"
Artificial Intelligence for Trading
Predicting Baseball Statistics: Classification and Regression Applications in Python Using scikit-learn
Portfolio Projects through my Data Science and Machine Learning Course program.
Machine Learning based web application which helps users to choose an appropriate insurance premuim for subscription by predicting it based on user's details like living style, gender, smoker or not etc.
[Springer Nature CS] Official code repository for the paper "A View Independent Classification Framework for Yoga Postures".
Combining phylogenetic networks and Random Forests for prediction of ancestry from multilocus genotype data
This is the repository used to make the project titled 'Grass Pollen in Cape Town: A Comparison of Generalised Additive Models and Random Forests' by Sky Cope and Chloë Stipinovich.
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