Feature engineering & interpretability for xgboost with board game ratings
This screencast (on more advanced modeling topics) uses #TidyTuesday data on board game ratings for custom feature engineering, tuning xgboost, and explainability methods. Check out the code on my blog: https://juliasilge.com/blog/board-games
▶︎
Predict ratings for chocolate with tidymodels
▶︎
To downsample or not? Handling class imbalance in bird feeder observations
▶︎
Machine Learning with R and TensorFlow
▶︎
Predict water availability in Sierra Leone with random forests
▶︎
Feature Engineering Secret From A Kaggle Grandmaster
▶︎
Predict astronauts' mission duration with tidymodels and bootstrap aggregation
▶︎
Machine Learning for Everybody – Full Course
▶︎
HPC with Slurm, R, and the slurmR R package
▶︎
Deep Dive into LLMs like ChatGPT
▶︎
Topic modeling for Spice Girls lyrics
▶︎
Data Analytics for Beginners | Data Analytics Training | Data Analytics Course | Intellipaat
▶︎
Python Project | Python Projects For Beginners | Python Project Tutorial | Intellipaat
▶︎
Dimensionality reduction with tidymodels for the Billboard Top 100
▶︎
Predict childcare costs in US counties with xgboost and early stopping
▶︎
Predict giant pumpkin weights with tidymodels workflowsets
▶︎
TidyModels vs Caret
▶︎
Power BI FULL COURSE for Beginners | Learn Dashboards & Reports Fast!
▶︎
TidyModels by Max Kuhn (2/24/2021)
▶︎
Weighted log odds ratios for haunted places in the US
▶︎