Introduction of the alternate features search using R, proposed in the paper. S. Hara, T. Maehara, Finding Alternate Features in Lasso, 1611.05940, 2016.
* Satoshi Hara and Kohei Hayashi. Making Tree Ensembles Interpretable: A Bayesian Model Selection Approach. AISTATS'18 (to appear).
arXiv ver.: https://arxiv.org/abs/1606.09066#
* GitHub
https://github.com/sato9hara/defragTrees
ERATO感謝祭 Season IV
【参考】Satoshi Hara and Takanori Maehara. Enumerate Lasso Solutions for Feature Selection. In Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI'17), pages 1985--1991, 2017.
Imputation of Missing Values using Random ForestSatoshi Kato
missForest packageの紹介
“MissForest - nonparametric missing value imputation for mixed-type data (DJ Stekhoven, P Bühlmann (2011), Bioinformatics 28 (1), 112-118)
* Satoshi Hara and Kohei Hayashi. Making Tree Ensembles Interpretable: A Bayesian Model Selection Approach. AISTATS'18 (to appear).
arXiv ver.: https://arxiv.org/abs/1606.09066#
* GitHub
https://github.com/sato9hara/defragTrees
ERATO感謝祭 Season IV
【参考】Satoshi Hara and Takanori Maehara. Enumerate Lasso Solutions for Feature Selection. In Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI'17), pages 1985--1991, 2017.
Imputation of Missing Values using Random ForestSatoshi Kato
missForest packageの紹介
“MissForest - nonparametric missing value imputation for mixed-type data (DJ Stekhoven, P Bühlmann (2011), Bioinformatics 28 (1), 112-118)
How to generate PowerPoint slides Non-manually using RSatoshi Kato
Introduction to:
- Basic idea and procedure of {officer} package
- Getting started: Embedding texts, tables and figures in slides
- PowerPoint Structure: Layouts and Placeholders
- Making a template for specific layouts
- Making a template for your own slide-layouts
Resources are avail at: https://github.com/katokohaku/powerpoint_with_officer
Exploratory data analysis using xgboost package in RSatoshi Kato
Explain HOW-TO procedure exploratory data analysis using xgboost (EDAXGB), such as feature importance, sensitivity analysis, feature contribution and feature interaction. It is just based on using built-in predict() function in R package.
All of the sample codes are available at: https://github.com/katokohaku/EDAxgboost
a Japanese introduction of an R package {featuretweakR }
available from: https://github.com/katokohaku/featureTweakR
reference: "Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking" (https://arxiv.org/abs/1706.06691). Codes are at my Github (https://github.com/katokohaku/feature_tweaking)
Outline of Genetic Algorithm + Searching for Maximum Value of Function and Traveling Salesman Problem using R.
To view source codes and animation:
Searching for Maximum Value of Function
- https://github.com/katokohaku/evolutional_comptutation/blob/master/chap2.1.Rmd
Traveling Salesman Problem
- https://github.com/katokohaku/evolutional_comptutation/blob/master/chap2.2.Rmd
Intoroduction & R implementation of "Interpretable predictions of tree-based ...Satoshi Kato
a Japanese introduction and an R implementation of "Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking" (https://arxiv.org/abs/1706.06691). Codes are at my Github (https://github.com/katokohaku/feature_tweaking)
Introduction of sensitivity analysis for randamforest regression, binary classification and multi-class classification of random forest using {forestFloor} package
Introduction of "the alternate features search" using R
1. 見落とされた変数に 救いの手を
“FindingAlternate Features in Lassos”
1.“Finding Alternate Features in Lassos”の一部をRでトレースします
(Satoshi Hara and Takanori Maehara (2016): "Finding Alternate Features in Lasso", in NIPS 2016 workshop on
Interpretable Machine Learning for Complex Systems, December 10th, 2016.)
2.{glmnet} で実装して使ってみます
Lassoにおける変数選択と代替候補となる変数の可視化
第58回R勉強会@東京(#TokyoR)
22. 使ってみた: Boston Housing Data Set
https://archive.ics.uci.edu/ml/datasets/Housing
http://pythondatascience.plavox.info/scikit-learn/scikit-learn に付属しているデータセット/
米国ボストン市郊外における
地域別の住宅価格のデータセット