Yorumlanabilir Makine Öğrenmesi
  • Yorumlanabilir Yapay Öğrenme
  • Bölüm 1 - Yazarın Önsözü
  • Bölüm 1.1 Çevirmenin Önsözü
  • Bölüm 2 - Giriş
    • 2.1 Hikaye Zamanı
    • 2.2 Makine Öğrenmesi Nedir?
    • 2.3 Terminoloji
  • 3. Yorumlanabilirlik
    • 3.1 Yorumlanabilirliğin Önemi
    • 3.2 Yorumlanabilirlik Yöntemlerinin Sınıflandırılması
    • 3.3 Yorumlanabilirliğin Kapsamı
    • 3.4 Yorumlanabilirliğin Değerlendirilmesi
    • 3.5 Açıklamaların Özellikleri
    • 3.6 İnsan Dostu Açıklamalar
  • 4 Veri Setleri
    • 4.1 Bisiklet Kiralama (Bike Rentals)- Regresyon
    • 4.2 Youtube Spam Yorumları (Metin Sınıflandırma)
    • 4.3 Rahim Ağzı Kanseri Risk Faktörleri (Sınıflandırma)
  • 5. Yorumlanabilir Modeller
    • 5.1 Doğrusal Regresyon (Linear Regression)
    • 5.2 Lojistik Regresyon (Logistic Regression)
    • 5.3 GLM, GAM ve Fazlası
    • 5.4 Karar Ağaçları (Decision Tre)
    • 5.5 Karar Kuralları (Decision Rules)
    • 5.6 RuleFit
    • 5.7 Diğer Yorumlanabilir Modeller
  • 6. Model Agnostik Metotlar (Model-Agnostic Methods)
  • 7. Örnek Tabanlı Açıklamalar (Example-Based Explanations)
  • 8.Küresel Model Agnostik Metotlar (Global Model-Agnostic Methods)
    • 8.1 Kısmi Bağımlılık Grafiği - Partial Dependency Plot
    • 8.2. Biriktirilmiş Yerel Etki (Accumulated Local Effects-ALE) Grafikleri
    • 8.3 Öznitelik Etkileşimi (Feature Interaction)
    • 8.4 Fonksiyonel Ayrıştırma (Functional Decomposition)
    • 8.5 Permütasyon Öznitelik Önemi (Permutation Feature Importance)
    • 8.6 Küresel Vekil Modeli (Global Surrogate)
    • 8.7 Prototipler ve Eleştiriler (Prototypes and Criticisms)
  • 9. Yerel Modelden Bağımsız Yöntemler (Local Model-Agnostic Methods)
    • 9.1 Bireysel Koşullu Beklenti (Individual Conditional Expectation)
    • 9.2 Yerel Vekil (Local Surrogate) (LIME)
    • 9.3 Karşıt Gerçekçi Açıklamalar (Counterfactual Explanations)
    • 9.4 Kapsamlı Kurallar (Scoped Rules (Anchors))
    • 9.5 Shapley Değerleri (Shapley Values)
    • 9.6 SHAP (SHapley Additive exPlanations)
  • 10. Sinir Ağları Yorumlaması
    • 10.1 Öğrenilmiş Özellikler (Learned Features)
    • 10.2 Piksel İlişkilendirmesi (Pixel Attribution)
    • 10.3 Kavramları Belirleme (Detecting Concepts)
    • 10.4 Kötü Amaçlı Örnekler (Adversarial Examples)
    • 10.5 Etkili Örnekler (Influential Instances)
  • 11. Kristal Küreye Bir Bakış
    • 11.1 Makine Öğrenmesinin Geleceği
      • 11.2 Yorumlanabilirliğin Geleceği
  • 12. Teşekkürler
  • Referanslar
  • Kullanılan R paketleri
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Kullanılan R paketleri

PreviousReferanslar

Last updated 5 months ago

arules. Hahsler M, Buchta C, Gruen B, Hornik K (2023). arules: Mining Association Rules and Frequent Itemsets. R package version 1.7-7, .

bookdown. Xie Y (2024). bookdown: Authoring Books and Technical Documents with R Markdown. R package version 0.40, .

Cairo. Urbanek S, Horner J (2023). Cairo: R Graphics Device using Cairo Graphics Library for Creating High-Quality Bitmap (PNG, JPEG, TIFF), Vector (PDF, SVG, PostScript) and Display (X11 and Win32) Output. R package version 1.6-2, .

caret. Kuhn M (2023). caret: Classification and Regression Training. R package version 6.0-94, .

data.table. Barrett T, Dowle M, Srinivasan A, Gorecki J, Chirico M, Hocking T (2024). data.table: Extension of data.frame. R package version 1.15.4, .

devtools. Wickham H, Hester J, Chang W, Bryan J (2022). devtools: Tools to Make Developing R Packages Easier. R package version 2.4.5, .

dplyr. Wickham H, François R, Henry L, Müller K, Vaughan D (2023). dplyr: A Grammar of Data Manipulation. R package version 1.1.4, .

DT. Xie Y, Cheng J, Tan X (2024). DT: A Wrapper of the JavaScript Library ‘DataTables’. R package version 0.33, .

e1071. Meyer D, Dimitriadou E, Hornik K, Weingessel A, Leisch F (2023). e1071: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien. R package version 1.7-14, .

ggplot2. Wickham H, Chang W, Henry L, Pedersen T, Takahashi K, Wilke C, Woo K, Yutani H, Dunnington D, van den Brand T (2024). ggplot2: Create Elegant Data Visualisations Using the Grammar of Graphics. R package version 3.5.1, .

grid. R Core Team (2024). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. .

gridExtra. Auguie B (2017). gridExtra: Miscellaneous Functions for “Grid” Graphics. R package version 2.3, .

iml. Casalicchio G, Molnar C, Schratz P (2024). iml: Interpretable Machine Learning. R package version 0.11.3, .

interactions. Long JA (2024). interactions: Comprehensive, User-Friendly Toolkit for Probing Interactions. R package version 1.2.0, .

jpeg. Urbanek S (2022). jpeg: Read and write JPEG images. R package version 0.1-10, .

jtools. Long JA (2023). jtools: Analysis and Presentation of Social Scientific Data. R package version 2.2.2, .

kableExtra. Zhu H (2024). kableExtra: Construct Complex Table with ‘kable’ and Pipe Syntax. R package version 1.4.0, .

knitr. Xie Y (2024). knitr: A General-Purpose Package for Dynamic Report Generation in R. R package version 1.48, .

latex2exp. Meschiari S (2022). latex2exp: Use LaTeX Expressions in Plots. R package version 0.9.6, .

lubridate. Spinu V, Grolemund G, Wickham H (2023). lubridate: Make Dealing with Dates a Little Easier. R package version 1.9.3, .

memoise. Wickham H, Hester J, Chang W, Müller K, Cook D (2021). memoise: ‘Memoisation’ of Functions. R package version 2.0.1, .

mgcv. Wood S (2023). mgcv: Mixed GAM Computation Vehicle with Automatic Smoothness Estimation. R package version 1.9-1, .

mlbench. Leisch F, Dimitriadou E (2024). mlbench: Machine Learning Benchmark Problems. R package version 2.1-5, .

mlr. Bischl B, Lang M, Kotthoff L, Schratz P, Schiffner J, Richter J, Jones Z, Casalicchio G, Gallo M, Binder M (2024). mlr: Machine Learning in R. R package version 2.19.2, .

numDeriv. Gilbert P, Varadhan R (2019). numDeriv: Accurate Numerical Derivatives. R package version 2016.8-1.1, .

OneR. von Jouanne-Diedrich H (2017). OneR: One Rule Machine Learning Classification Algorithm with Enhancements. R package version 2.2, .

party. Hothorn T, Hornik K, Strobl C, Zeileis A (2024). party: A Laboratory for Recursive Partytioning. R package version 1.3-16, .

partykit. Hothorn T, Zeileis A (2024). partykit: A Toolkit for Recursive Partytioning. R package version 1.2-21, .

patchwork. Pedersen T (2024). patchwork: The Composer of Plots. R package version 1.2.0, .

png. Urbanek S (2022). png: Read and write PNG images. R package version 0.1-8, .

pre. Fokkema M, Christoffersen B (2024). pre: Prediction Rule Ensembles. R package version 1.0.7, .

R.utils. Bengtsson H (2023). R.utils: Various Programming Utilities. R package version 2.12.3, .

randomForest. Breiman FobL, Cutler A, Liaw RpbA, Wiener. M (2022). randomForest: Breiman and Cutler’s Random Forests for Classification and Regression. R package version 4.7-1.1, .

readr. Wickham H, Hester J, Bryan J (2024). readr: Read Rectangular Text Data. R package version 2.1.5, .

rjson. Couture-Beil A (2022). rjson: JSON for R. R package version 0.2.21, .

roxygen2. Wickham H, Danenberg P, Csárdi G, Eugster M (2024). roxygen2: In-Line Documentation for R. R package version 7.3.2, .

rpart. Therneau T, Atkinson B (2023). rpart: Recursive Partitioning and Regression Trees. R package version 4.1.23, .

RWeka. Hornik K (2023). RWeka: R/Weka Interface. R package version 0.4-46, .

shiny. Chang W, Cheng J, Allaire J, Sievert C, Schloerke B, Xie Y, Allen J, McPherson J, Dipert A, Borges B (2024). shiny: Web Application Framework for R. R package version 1.9.0, .

svglite. Wickham H, Henry L, Pedersen T, Luciani T, Decorde M, Lise V (2023). svglite: An ‘SVG’ Graphics Device. R package version 2.1.3, .

tidyr. Wickham H, Vaughan D, Girlich M (2024). tidyr: Tidy Messy Data. R package version 1.3.1, .

tm. Feinerer I, Hornik K (2024). tm: Text Mining Package. R package version 0.7-13, .

viridis. Garnier S (2024). viridis: Colorblind-Friendly Color Maps for R. R package version 0.6.5, .

xgboost. Chen T, He T, Benesty M, Khotilovich V, Tang Y, Cho H, Chen K, Mitchell R, Cano I, Zhou T, Li M, Xie J, Lin M, Geng Y, Li Y, Yuan J (2024). xgboost: Extreme Gradient Boosting. R package version 1.7.8.1, .

yaImpute. Evans J, Crookston N, Finley A (2023). yaImpute: Nearest Neighbor Observation Imputation and Evaluation Tools. R package version 1.0-34, .

https://CRAN.R-project.org/package=arules
https://CRAN.R-project.org/package=bookdown
https://CRAN.R-project.org/package=Cairo
https://CRAN.R-project.org/package=caret
https://CRAN.R-project.org/package=data.table
https://CRAN.R-project.org/package=devtools
https://CRAN.R-project.org/package=dplyr
https://CRAN.R-project.org/package=DT
https://CRAN.R-project.org/package=e1071
https://CRAN.R-project.org/package=ggplot2
https://www.R-project.org/
https://CRAN.R-project.org/package=gridExtra
https://CRAN.R-project.org/package=iml
https://CRAN.R-project.org/package=interactions
https://CRAN.R-project.org/package=jpeg
https://CRAN.R-project.org/package=jtools
https://CRAN.R-project.org/package=kableExtra
https://CRAN.R-project.org/package=knitr
https://CRAN.R-project.org/package=latex2exp
https://CRAN.R-project.org/package=lubridate
https://CRAN.R-project.org/package=memoise
https://CRAN.R-project.org/package=mgcv
https://CRAN.R-project.org/package=mlbench
https://CRAN.R-project.org/package=mlr
https://CRAN.R-project.org/package=numDeriv
https://CRAN.R-project.org/package=OneR
https://CRAN.R-project.org/package=party
https://CRAN.R-project.org/package=partykit
https://CRAN.R-project.org/package=patchwork
https://CRAN.R-project.org/package=png
https://CRAN.R-project.org/package=pre
https://CRAN.R-project.org/package=R.utils
https://CRAN.R-project.org/package=randomForest
https://CRAN.R-project.org/package=readr
https://CRAN.R-project.org/package=rjson
https://CRAN.R-project.org/package=roxygen2
https://CRAN.R-project.org/package=rpart
https://CRAN.R-project.org/package=RWeka
https://CRAN.R-project.org/package=shiny
https://CRAN.R-project.org/package=svglite
https://CRAN.R-project.org/package=tidyr
https://CRAN.R-project.org/package=tm
https://CRAN.R-project.org/package=viridis
https://CRAN.R-project.org/package=xgboost
https://CRAN.R-project.org/package=yaImpute