SuperLearner guide: fitting models, ensembling, prediction, hyperparameters, parallelization, timing, feature selection, etc.
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Updated
Apr 17, 2019 - HTML
SuperLearner guide: fitting models, ensembling, prediction, hyperparameters, parallelization, timing, feature selection, etc.
Modelling with Tidymodels and Parsnip - A Tidy Approach to a Classification Problem
Introduction to Statistical Learning
A DataCamp course for the University of Helsinki
Data and R scripts accompanying the paper "Forecasting deforestation in the Brazilian Amazon to prioritize conservation efforts"
k-fold cross validation for factor analysis
Creating Predictions for Numerai with Keras and scikit-learn
Rcode for workshop on cross-validation in the Harvard Methods Dinner talk series
Support Vector Machine : Theoretical Development with loss function and K-Fold Cross Validation
Scripts for Anti-HLA antibody target prediction via machine learning
Toxic Comment Classification Project constructed by Qimo Li, Chen He and Kun Qiu for the course "Introduction to Natural Language Processing in Python" at Brandeis University.
Files for compiling my presentation about H2O.ai.
Built a model to predict the value of a given house in the Boston real estate market using various statistical analysis tools. Identified the best price that a client can sell their house utilizing machine learning.
A library of implementations in the 'iads' directory, plus Jupyter notebooks for testing
Predicting Next Booking Destinations for Airbnb Users. Feel free to access the Streamlit App in the link below.
Udacity Machine Learning Nano degree Program. Project Predicting House prices in Boston
Flight price Prediction is made using decision tree model and Machine learning concepts
Visualizes the data, builds a multi-linear regression model, applies a 10-fold cross-validation resampling method, and evaluates LM, SVM, and KNN model performance using R
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