19 Sep r iml shapley
#' # Shapley() also works with multiclass classification#' @title Prediction explanations with game theory An alternative for explaining individual predictions is a method from coalitional game theory named Shapley value.
Name : Description : Partial: Effect of one or two feature(s) on the model predictions (deprecated) FeatureImp: Feature importance: InterpretationMethod This novel approach allows us to dig a little bit more in the complexity of the predictive model results, while it allows us to explore the relationships between variables for predicted case. Ignored for#' # First we fit a machine learning model on the Boston housing data#' shapley <- Shapley$new(mod, x.interest = X[1, ])
The iml package is probably the most robust ML interpretability package available.
Although the interaction functions are notably slow, the other functions are faster or comparable … Shapley value for explaining single predictions; In order to work with iml, we need to adapt our data a bit by removing the response variable and the creating a new predictor object that holds the model, the data and the class labels. An alternative for explaining individual predictions is a method from coalitional game theory named Shapley value. Strumbelj, E., Kononenko, I. Qiita can be used more conveniently after logging in.#7 ptratio 1.8601196 2.006714 2.251311 4.125215X軸のマークは'lstat'の分布を表しており、どの領域が関係性が高いかを示しています。#4 0.03237 0 2.18 0 0.458 6.998 45.8 6.0622 3 222 18.7 394.63 2.94#2 rm 10.9331383 11.489301 19.441368 23.618625#6 rm -0.450538504 15.837141138 rm=6.575#ptratio -0.5244767 15.300 -8.024493 15.3 ptratio ptratio=15.3# feature phi phi.var feature.value#rm 4.4836483 6.575 29.479987 6.575 rm rm=6.575#12 chas 1.0462975 1.126355 1.171892 2.315455今後も説明可能なAIの需要は高まっていき、次々と新しい理論・実装が現れてくることが予想されます。ランダムフォレストモデルの作成は目的変数、使用データセット、木の深さを指定するだけで簡単にできます。# self$predictor$data$match_cols(data.frame(newdata)) で: #6 0.02985 0 2.18 0 0.458 6.430 58.7 6.0622 3 222 18.7 394.12 5.21How to use in R model-agnostic data explanation with DALEX & iml#8 age 1.5103398 1.827092 2.179101 3.755965#1 0.00632 18 2.31 0 0.538 6.575 65.2 4.0900 1 296 15.3 396.90 4.98#5 nox -0.151510825 1.830613219 nox=0.538#4 nox 2.8722394 3.003836 3.498187 6.175004H-statisticの詳しい説明はここでは割愛しますが、H-statisticは[0,1]の無次元量であるため、異なる特徴量・モデル間の比較に使うことができます。ゲーム理論を使って各変数の貢献度を計算しよう、という少し今までとはテイストの違う考え方がShapley Valueです。#6 indus 1.8486110 2.099748 2.274380 4.316464この例ではランダムフォレストを決定木に変換していますが、あらゆるブラックボックスモデルに適用可能です。#1 crim -0.317960865 2.046510169 crim=0.00632近年のAI・機械学習ブームの一方で、一部の予測モデルのブラックボックス的な性質ゆえに、AIへの過剰な期待・無責任なAI利用への警鐘を鳴らす声も頻繁に聞かれるようになりました。Introduction to iml: Interpretable Machine Learning in RSign up for free and join this conversation.ALEと似た概念にPDP (Partial Dependence Plot)がありますが、PDPでは「あるデータに対して、注目する特徴量（変数）以外はすべて同じ値をもつデータがたくさんあったとき、対象の特徴量（変数）の値の増減によって予測値がどう変化するか？」ということを調べます。 #' mod <- Predictor$new(rf, data = X, type = "prob", class = "setosa")#' `Shapley` computes feature contributions for single predictions with the#' X <- Boston[-which(names(Boston) == "medv")]#' The number of times coalitions/marginals are#' Strumbelj, E., Kononenko, I.
https://christophm.github.io/interpretable-ml-book/shapley.html
Embedding an R snippet on your website Shapley values are implemented in both the iml and fastshap packages for R. SHAP, an alternative estimation method for Shapley values, is presented in the next chapter. (2018)のFeature importanceや、 Apley (2018) のaccumulated local effects plotsといった、説明可能なAIに関する技術が実装されています。 imlでできること The Shapley value fairly distributes the difference of the instance's prediction and the datasets average prediction among the features. Interpretability methods to analyze the behavior and predictions of any machine learning model. Shapley computes feature contributions for single predictions with the Shapley value, an approach from cooperative game theory. Explaining prediction models and#' # Then we explain the first instance of the dataset with the Shapley method:#' Single row with the instance to be explained.#' Systems, 41(3), 647-665. https://doi.org/10.1007/s10115-013-0679-x#' Should the feature values be sorted by Shapley value?
The features values of an instance cooperate to achieve the prediction. iml.
By following users and tags, you can catch up information on technical fields that you are interested in as a whole#5 crim 2.4794144 2.856576 2.971373 5.872280#4 chas -0.006707333 0.002755066 chas=0Why do not you register as a user and use Qiita more conveniently?#11 rad 1.0344114 1.298408 1.396144 2.669146近似が単純な分、得られる説明が短く分かりやすいのが利点ですが、ブラックボックスモデルの複雑性に依存して出力が安定せず、サンプルプロセスを繰り返すと出力が変わる可能性がある点には注意が必要です。#3 0.02729 0 7.07 0 0.469 7.185 61.1 4.9671 2 242 17.8 392.83 4.03FeatureImp\$new()を呼び出すことで、どの特徴量（変数）が重要であるかを測ることができます。#9 tax 1.5761642 1.771273 2.111650 3.641216#2 zn -0.036996333 0.024963557 zn=18You seem to be reading articles frequently this month. The features values of an instance cooperate to achieve the prediction. Features. BreakDown also shows the contributions of each feature to the prediction, but computes them step by step. Package overview
Description. (2014). # Then we explain the first instance of the dataset with the Shapley method:A different way to explain predictions: LocalModel Qiita can be used more conveniently after logging in.Help us understand the problem. I am currently working on a For loop in R. If I run the For loop on my own data, it takes ages, and I believe because I did something inefficient in my code. In iml: Interpretable Machine Learning. #' distributes the difference of the instance's prediction and the datasets#' Shapley value, an approach from cooperative game theory. The objects of this class are cloneable with this method. The Shapley value fairly"Actual prediction: %.2f\nAverage prediction: %.2f"For more information on customizing the embed code, read (LIME and Shapley value)Besides knowing which features were important, we are interested in how the features influence the predicted outcome.
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