Interpretable machine learning: a guide for making black box models explainable
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Munich
Christoph Molnar
2022
|
| Edition: | Second edition |
| Subjects: | |
| Online Access: | kostenfrei Inhaltsverzeichnis Inhaltsverzeichnis |
| Physical Description: | x, 317 Seiten Illustrationen, Diagramme |
Staff View
MARC
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| 245 | 1 | 0 | |a Interpretable machine learning |b a guide for making black box models explainable |c Christoph Molnar |
| 250 | |a Second edition | ||
| 264 | 1 | |a Munich |b Christoph Molnar |c 2022 | |
| 300 | |a x, 317 Seiten |b Illustrationen, Diagramme | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
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| 856 | 4 | 1 | |u https://christophm.github.io/interpretable-ml-book/ |x Verlag |z kostenfrei |3 Volltext |
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| 912 | |a ebook | ||
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Record in the Search Index
| _version_ | 1825026914878750720 |
|---|---|
| adam_text |
CONTENTS
PREFACE
BY
THE
AUTHOR
IX
1
INTRODUCTION
1
1.1
STORY
TIME
.
3
1.2
WHAT
IS
MACHINE
LEARNING?
.
9
1.3
TERMINOLOGY
.
10
2
INTERPRETABILITY
13
2.1
IMPORTANCE
OF
INTERPRETABILITY
.
13
2.2
TAXONOMY
OF
INTERPRETABILITY
METHODS
.
18
2.3
SCOPE
OF
INTERPRETABILITY
.
20
2.4
EVALUATION
OF
INTERPRETABILITY
.
22
2.5
PROPERTIES
OF
EXPLANATIONS
.
22
2.6
HUMAN-FRIENDLY
EXPLANATIONS
.
25
3
DATASETS
31
3.1
BIKE
RENTALS
(REGRESSION)
.
31
3.2
YOUTUBE
SPAM
COMMENTS
(TEXT
CLASSIFICATION)
.
32
3.3
RISK
FACTORS
FOR
CERVICAL
CANCER
(CLASSIFICATION)
.
33
4
INTERPRETABLE
MODELS
35
4.1
LINEAR
REGRESSION
.
37
4.2
LOGISTIC
REGRESSION
.
52
4.3
GLM,
GAM
AND
MORE
.
58
4.4
DECISION
TREE
.
76
4.5
DECISION
RULES
.
82
4.6
RULEFIT
.
98
4.7
OTHER
INTERPRETABLE
MODELS
.
106
5
MODEL-AGNOSTIC
METHODS
109
6
EXAMPLE-BASED
EXPLANATIONS
113
7
GLOBAL
MODEL-AGNOSTIC
METHODS
115
7.1
PARTIAL
DEPENDENCE
PLOT
(PDP)
.
116
7.2
ACCUMULATED
LOCAL
EFFECTS
(ALE)
PLOT
.
122
7.3
FEATURE
INTERACTION
.
140
7.4
FUNCTIONAL
DECOMPOSITON
.
147
7.5
PERMUTATION
FEATURE
IMPORTANCE
.
157
7.6
GLOBAL
SURROGATE
.
165
7.7
PROTOTYPES
AND
CRITICISMS
.
170
8
LOCAL
MODEL-AGNOSTIC
METHODS
179
8.1
INDIVIDUAL
CONDITIONAL
EXPECTATION
(ICE)
.
180
8.2
LOCAL
SURROGATE
(LIME)
.
185
8.3
COUNTERFACTUAL
EXPLANATIONS
.
194
8.4
SCOPED
RULES
(ANCHORS)
.
205
8.5
SHAPLEY
VALUES
.
215
8.6
SHAP
(SHAPLEY
ADDITIVE
EXPLANATIONS)
.
227
9
NEURAL
NETWORK
INTERPRETATION
241
9.1
LEARNED
FEATURES
.
243
9.2
PIXEL
ATTRIBUTION
(SALIENCY
MAPS)
.
254
9.3
DETECTING
CONCEPTS
.
265
9.4
ADVERSARIAL
EXAMPLES
.
270
9.5
INFLUENTIAL
INSTANCES
.
279
10
A
LOOK
INTO
THE
CRYSTAL
BALL
295
10.1
THE
FUTURE
OF
MACHINE
LEARNING
.
296
10.2
THE
FUTURE
OF
INTERPRETABILITY
.
298
11
CONTRIBUTE
TO
THE
BOOK
301
12
CITING
THIS
BOOK
303
13
TRANSLATIONS
305
14
ACKNOWLEDGEMENTS
307 |
| adam_txt |
CONTENTS
PREFACE
BY
THE
AUTHOR
IX
1
INTRODUCTION
1
1.1
STORY
TIME
.
3
1.2
WHAT
IS
MACHINE
LEARNING?
.
9
1.3
TERMINOLOGY
.
10
2
INTERPRETABILITY
13
2.1
IMPORTANCE
OF
INTERPRETABILITY
.
13
2.2
TAXONOMY
OF
INTERPRETABILITY
METHODS
.
18
2.3
SCOPE
OF
INTERPRETABILITY
.
20
2.4
EVALUATION
OF
INTERPRETABILITY
.
22
2.5
PROPERTIES
OF
EXPLANATIONS
.
22
2.6
HUMAN-FRIENDLY
EXPLANATIONS
.
25
3
DATASETS
31
3.1
BIKE
RENTALS
(REGRESSION)
.
31
3.2
YOUTUBE
SPAM
COMMENTS
(TEXT
CLASSIFICATION)
.
32
3.3
RISK
FACTORS
FOR
CERVICAL
CANCER
(CLASSIFICATION)
.
33
4
INTERPRETABLE
MODELS
35
4.1
LINEAR
REGRESSION
.
37
4.2
LOGISTIC
REGRESSION
.
52
4.3
GLM,
GAM
AND
MORE
.
58
4.4
DECISION
TREE
.
76
4.5
DECISION
RULES
.
82
4.6
RULEFIT
.
98
4.7
OTHER
INTERPRETABLE
MODELS
.
106
5
MODEL-AGNOSTIC
METHODS
109
6
EXAMPLE-BASED
EXPLANATIONS
113
7
GLOBAL
MODEL-AGNOSTIC
METHODS
115
7.1
PARTIAL
DEPENDENCE
PLOT
(PDP)
.
116
7.2
ACCUMULATED
LOCAL
EFFECTS
(ALE)
PLOT
.
122
7.3
FEATURE
INTERACTION
.
140
7.4
FUNCTIONAL
DECOMPOSITON
.
147
7.5
PERMUTATION
FEATURE
IMPORTANCE
.
157
7.6
GLOBAL
SURROGATE
.
165
7.7
PROTOTYPES
AND
CRITICISMS
.
170
8
LOCAL
MODEL-AGNOSTIC
METHODS
179
8.1
INDIVIDUAL
CONDITIONAL
EXPECTATION
(ICE)
.
180
8.2
LOCAL
SURROGATE
(LIME)
.
185
8.3
COUNTERFACTUAL
EXPLANATIONS
.
194
8.4
SCOPED
RULES
(ANCHORS)
.
205
8.5
SHAPLEY
VALUES
.
215
8.6
SHAP
(SHAPLEY
ADDITIVE
EXPLANATIONS)
.
227
9
NEURAL
NETWORK
INTERPRETATION
241
9.1
LEARNED
FEATURES
.
243
9.2
PIXEL
ATTRIBUTION
(SALIENCY
MAPS)
.
254
9.3
DETECTING
CONCEPTS
.
265
9.4
ADVERSARIAL
EXAMPLES
.
270
9.5
INFLUENTIAL
INSTANCES
.
279
10
A
LOOK
INTO
THE
CRYSTAL
BALL
295
10.1
THE
FUTURE
OF
MACHINE
LEARNING
.
296
10.2
THE
FUTURE
OF
INTERPRETABILITY
.
298
11
CONTRIBUTE
TO
THE
BOOK
301
12
CITING
THIS
BOOK
303
13
TRANSLATIONS
305
14
ACKNOWLEDGEMENTS
307 |
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| id | DE-604.BV047910125 |
| illustrated | Illustrated |
| index_date | 2024-07-03T19:31:06Z |
| indexdate | 2025-02-25T11:01:35Z |
| institution | BVB |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033291908 |
| oclc_num | 1310244840 |
| open_access_boolean | 1 |
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| physical | x, 317 Seiten Illustrationen, Diagramme |
| psigel | ebook |
| publishDate | 2022 |
| publishDateSearch | 2022 |
| publishDateSort | 2022 |
| publisher | Christoph Molnar |
| record_format | marc |
| spelling | Molnar, Christoph 1989- Verfasser (DE-588)1222925737 aut Interpretable machine learning a guide for making black box models explainable Christoph Molnar Second edition Munich Christoph Molnar 2022 x, 317 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 s DE-604 Erscheint auch als Online-Ausgabe https://leanpub.com/interpretable-machine-learning https://christophm.github.io/interpretable-ml-book/ Verlag kostenfrei Volltext B:DE-101 application/pdf https://d-nb.info/1254174508/04 Inhaltsverzeichnis DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033291908&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Molnar, Christoph 1989- Interpretable machine learning a guide for making black box models explainable Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4193754-5 |
| title | Interpretable machine learning a guide for making black box models explainable |
| title_auth | Interpretable machine learning a guide for making black box models explainable |
| title_exact_search | Interpretable machine learning a guide for making black box models explainable |
| title_exact_search_txtP | Interpretable machine learning a guide for making black box models explainable |
| title_full | Interpretable machine learning a guide for making black box models explainable Christoph Molnar |
| title_fullStr | Interpretable machine learning a guide for making black box models explainable Christoph Molnar |
| title_full_unstemmed | Interpretable machine learning a guide for making black box models explainable Christoph Molnar |
| title_short | Interpretable machine learning |
| title_sort | interpretable machine learning a guide for making black box models explainable |
| title_sub | a guide for making black box models explainable |
| topic | Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Maschinelles Lernen |
| url | https://christophm.github.io/interpretable-ml-book/ https://d-nb.info/1254174508/04 http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033291908&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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