Explanatory Model Analysis

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A01=Przemyslaw Biecek
A01=Tomasz Burzykowski
advanced model assessment strategies
Agnostic
Auditable Machine Learning
Author_Przemyslaw Biecek
Author_Tomasz Burzykowski
Black Box Model
Categorical Dependent Variable
Category=PBT
Category=UY
Category=UYQ
classification algorithms
Code Snippets
Continuous Explanatory Variables
Data Frame
data science methods
dataset-level exploration
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explainable artificial intelligence
eXplainable Artificial Intelligence (XAI)
explanatory model analysis
Explanatory Variables
feature attribution analysis
goodness-of-prediction
instance-level exploration
Interpretable Machine Learning
Left Hand Side Panel
Left Hand Side Plot
Linear Regression Model
Loss Function
machine learning
Model Agnostic Explanations
model interpretability tools
model validation
Model Visualisation
model-agnostic approach
Predicted Survival Probability
predictive analytics techniques
predictive models
Random Forest
Random Forest Model
regression diagnostics
Regression Model
Roc Curve
Scatter Plot
Shapley Values
statistical modelling
SVM
SVM Model
Titanic Data
Titanic Dataset
Variable Importance Measure
Variable Importance Plots
Variable's Attribution
Variable’s Attribution

Product details

  • ISBN 9780367693923
  • Weight: 460g
  • Dimensions: 156 x 234mm
  • Publication Date: 26 Sep 2022
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
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Explanatory Model Analysis Explore, Explain and Examine Predictive Models is a set of methods and tools designed to build better predictive models and to monitor their behaviour in a changing environment. Today, the true bottleneck in predictive modelling is neither the lack of data, nor the lack of computational power, nor inadequate algorithms, nor the lack of flexible models. It is the lack of tools for model exploration (extraction of relationships learned by the model), model explanation (understanding the key factors influencing model decisions) and model examination (identification of model weaknesses and evaluation of model's performance). This book presents a collection of model agnostic methods that may be used for any black-box model together with real-world applications to classification and regression problems.

Przemyslaw Biecek is a professor in human-oriented machine learning at the Warsaw University of Technology and Principal Data Scientist in Samsung R&D Institute Poland. His main research project is DrWhy.AI - tools and methods for exploration, explanation, visualisation, and debugging of predictive models.

Tomasz Burzykowski is professor of biostatistics at Hasselt University and Vice-President for Research at International Drug Development Institute (IDDI). He has published extensively on applications of statistics in medicine and biology.

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