Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
Confusion matrix
Search
Sunmi Yoon
November 03, 2019
Technology
0
150
Confusion matrix
Confusion matrix 기초부터 머신러닝 응용까지 for dataitgirls3
Sunmi Yoon
November 03, 2019
Tweet
Share
More Decks by Sunmi Yoon
See All by Sunmi Yoon
데이터 분석가 채용 공고 읽는 방법
ysunmi0427
1
320
Deep down in classification 0.5 magic number
ysunmi0427
0
91
Tree Methods
ysunmi0427
0
120
심슨의 역설
ysunmi0427
0
2.1k
회사는 어떤 사람을 데이터 분석가로 채용하고 싶어하는 것일까?
ysunmi0427
0
2.2k
Other Decks in Technology
See All in Technology
Как мы автоматизировали интеграционное тестирование с Gonkey и не пожалели. Паша Егорычев, Кирилл Поляков
lamodatech
0
2k
RubyKaigi NOC 近況 2025
sorah
1
600
コードや知識を組み込む / Incorporating Codes and Knowledge
ks91
PRO
0
170
DjangoCon Europe 2025 Keynote - Django for Data Science
wsvincent
0
520
Part2 GitHub Copilotってなんだろう
tomokusaba
2
720
ビジネスとデザインとエンジニアリングを繋ぐために 一人のエンジニアは何ができるか / What can a single engineer do to connect business, design, and engineering?
kaminashi
2
890
10分で学ぶ、RAGの仕組みと実践
supermarimobros
0
900
データベース04: SQL (1/3) 単純質問 & 集約演算
trycycle
PRO
0
730
エンジニアリングで組織のアウトカムを最速で最大化する!
ham0215
1
300
Notion x ポストモーテムで広げる組織の学び / Notion x Postmortem
isaoshimizu
1
160
Computer Use〜OpenAIとAnthropicの比較と将来の展望〜
pharma_x_tech
6
1k
AWSを利用する上で知っておきたい名前解決の話
nagisa53
6
780
Featured
See All Featured
The Invisible Side of Design
smashingmag
299
50k
Fantastic passwords and where to find them - at NoRuKo
philnash
51
3.2k
Making Projects Easy
brettharned
116
6.2k
Learning to Love Humans: Emotional Interface Design
aarron
273
40k
The MySQL Ecosystem @ GitHub 2015
samlambert
251
12k
Design and Strategy: How to Deal with People Who Don’t "Get" Design
morganepeng
129
19k
We Have a Design System, Now What?
morganepeng
52
7.6k
CoffeeScript is Beautiful & I Never Want to Write Plain JavaScript Again
sstephenson
160
15k
Intergalactic Javascript Robots from Outer Space
tanoku
271
27k
Put a Button on it: Removing Barriers to Going Fast.
kastner
60
3.8k
GraphQLとの向き合い方2022年版
quramy
46
14k
Unsuck your backbone
ammeep
671
58k
Transcript
Evaluation for classification dataitgirls3 Instructor Sunmi Yoon
Confusion Matrix
https://sumniya.tistory.com/26
Evaluation Metrics from Confusion Matrix
https://towardsdatascience.com/understanding-confusion-matrix-a9ad42dcfd62
Precision(ب), PPV(Positive Predictive Value) ݽ؛ TrueۄҊ ࠙ܨೠ Ѫ ী, पઁ
Trueੋ Ѫ ࠺ਯ Recall(അਯ), Sensitivity, hit rate पઁ True ী ݽ؛ True۽ ࠙ܨೠ ࠺ਯ “Precision݅ न҃ਸ ॳݶ ݽ؛ ੋ࢝೧Ҋ, Recall݅ न҃ॳݶ ݽ؛ ಌ” ܳ ࢤп೧ࠁࣁਃ.
Accuracy TP, TNਸ ݽف Ҋ۰ೞח . Label ࠛӐഋ बೡ ٸী
ࢎਊਸ ೧ঠ פ. F1 Score Precisionҗ Recall ઑചಣӐ Label ࠛӐഋ बೡ ٸী ݽ؛ ࢿמਸ ഛೞѱ ಣоೡ ࣻ णפ. Label ࠛӐഋ बೡ ٸী, Accuracyח ۽ࢲ न܉ࢿਸ णפ. ਬܳ ࢤп ೧ ࠁࣁਃ.
https://sumniya.tistory.com/26 ৵ ࣿಣӐ ইפҊ ઑചಣӐੋо?
ઑӘ݅ ؊ о ࠇद
https://towardsdatascience.com/understanding-confusion-matrix-a9ad42dcfd62 द Ӓܿਵ۽ جই৬ࢲ, ଘ ফܳ बਵ۽ ࢤп೮
https://towardsdatascience.com/understanding-confusion-matrix-a9ad42dcfd62 द Ӓܿਵ۽ جই৬ࢲ, ߣূ ফب э ࢤпೞݶࢲ ࠇद
(Әࠗఠ ഁтܾ ࣻ )
TRUE FALSE ࠙ܨѾҗ TRUE TP FP FALSE FN TN
TRUE FALSE ࠙ܨѾҗ TRUE TP FP FALSE FN TN
Precision Positive Predictive Value ࠙ܨ Ѿҗ(ݽ؛)ਸ बਵ۽
TRUE FALSE ࠙ܨѾҗ TRUE TP FP FALSE FN TN
Negative Predictive Value ࠙ܨ Ѿҗ(ݽ؛)ਸ बਵ۽
TRUE FALSE ࠙ܨѾҗ TRUE TP FP FALSE FN TN
Recall Sensitivity True Positive Rate ਸ बਵ۽
TRUE FALSE ࠙ܨѾҗ TRUE TP FP FALSE FN TN
ਸ बਵ۽ False Positive Rate
TRUE FALSE ࠙ܨѾҗ TRUE TP FP FALSE FN TN
ਸ बਵ۽ Specificity True Negative Rate
TRUE FALSE ࠙ܨѾҗ TRUE TP FP FALSE FN TN
ਸ बਵ۽ Fall-out rate False Positive Rate
https://towardsdatascience.com/understanding-confusion-matrix-a9ad42dcfd62 Ѧ ೞҊ ೮ભ. ߣূ ফب э ࢤпೞݶࢲ ࠇद (Әࠗఠ
ഁтܾ ࣻ )
TRUE FALSE ࠙ܨѾҗ TRUE TP FP FALSE FN TN
? TP ब ٜ ܻೞݶ, ?
TRUE FALSE ࠙ܨѾҗ TRUE TP FP FALSE FN TN
TN ब ٜ ? ܻೞݶ, ?
ഁтܻભ? ਗې Ӓ۠Ѣਃ
ӝୡח ೮ਵפө ઑӘ݅ ؊ ೧ ࠇद.
Confusion Matrix with Histogram
https://www.medcalc.org/manual/roc-curves.php Criterion, Threshold য়ܲଃ Distribution Actual True, ৽ଃ Actual False.
Threshold ਤ۽ח ݽف True۽ ஏೞח ݽ؛ Ҋ о೮ਸ ٸ,
https://www.medcalc.org/manual/roc-curves.php Thresholdܳ ӓױਵ۽ ஏ ز दெࠇद. যڃ ੌ ੌযաաਃ? Precision:
Recall: Specificity: Fall-out:
https://www.medcalc.org/manual/roc-curves.php Thresholdܳ ӓױਵ۽ ஏ ز दெࠇद. যڃ ੌ ੌযաաਃ? True
positive rate: True negative rate:
https://www.medcalc.org/manual/roc-curves.php ߣূ ߈۽ ز दெࠇद. যڃ ੌ ੌযաաਃ? True positive
rate: True negative rate:
Specificity৬ Sensitivity ҙ҅ https://www.medcalc.org/manual/roc-curves.php
ROC(Receiver Operating Characteristic) curve
рױೞѱח, Sensitivity৬ 1-Specificityܳ п ୷ਵ۽ ೞח 2ରਗ Ӓې https://www.medcalc.org/manual/roc-curves.php AUC
(Area Under Curve)
рױೞѱח, Sensitivity৬ 1-Specificityܳ п ୷ਵ۽ ೞח 2ରਗ Ӓې https://www.medcalc.org/manual/roc-curves.php Actual
True৬ Actual False distribution ৮߷ೞѱ эਸ ٸ (feature class ߸߹מ۱ হ) ROC curveח 45ب пب ࢶ
рױೞѱח, Sensitivity৬ 1-Specificityܳ п ୷ਵ۽ ೞח 2ରਗ Ӓې https://www.medcalc.org/manual/roc-curves.php Actual
True৬ Actual False distribution Ҁח হ ৮߷ೞѱ ܻ࠙ ؼ ٸ ROC ழ࠳ (feature class ߸߹ מ۱ ৮߷) ROC ழ࠳о ઝ࢚ױী оөࣻ۾ feature class ߸߹ מ۱ જҊ ೡ ࣻ .
ROC(Receiver Operating Characteristic) curve with Machine Learning
Classifierܳ ݅ٚח Ѥ, ف ѐ histogramਸ ӒܻҊ Thresholdܳ ೞח Ѫ
https://www.medcalc.org/manual/roc-curves.php
https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html#sphx-glr-auto-examples-model-selection-plot-roc-py Histogramਸ Ӓ۷ח Ѥ ROC ழ࠳ܳ Ӓܾ ࣻ ח Ѫ!
https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html#sphx-glr-auto-examples-model-selection-plot-roc-py ROC ழ࠳ܳ Ӓܾ ࣻ ח Ѥ ৈ۞ ROC ழ࠳
р ࠺Үܳ ా೧ જ ࢿמ ݽ؛ਸ ইյ ࣻ ח Ѫ!
AUCо = ݽ؛ ҅ೠ probabilityܳ ߄ఔਵ۽ Ӓܽ histogramٜ ੜ
ܻ࠙غয . = ݽ؛ Threshold(Decision BoundaryۄҊب ೠ)ী ؏ хೞ. = উੋ ஏਸ ೠ.
ݽ؛ ࢶఖী ROC ழ࠳ܳ ഝਊೠ = Decision Boundaryী ࢚ҙহ ؊
જ ݽ؛ਸ ח. = ganziо դ.
Ӓ۰ࠇद. ؘఠ: titanic ݽ؛ - sklearn.linear_model.LinearRegression - sklearn.linear_model.LogisticRegression -
sklearn.tree.DecisionTreeClassifier - sklearn.ensemble.RandomForestClassifier ١ whatever you want - Tree ҅ৌ ݽ؛ ҃ model predict_proba() ݫࣗ٘ܳ ࢎਊೞݶ ഛܫ ҅ ؾ פ. - ীח Thresholdܳ a ݅ఀ ز೧оݴ Sensitivity, Specificityܳ ҅೧ ઝܳ ҳೞ ࣁਃ. - যڌѱ ೞݶ Thresholdܳ ੜ زदఃݶࢲ ROC ઝܳ ନਸ ࣻ ਸөਃ? - ઝٜਸ ಣݶ࢚ী ନযࠁࣁਃ.
sklearn.metrics.roc_curve ܳ ഝਊ ೧ ࠇद. ؘఠ: titanic ݽ؛ - sklearn.linear_model.LinearRegression
- sklearn.linear_model.LogisticRegression - sklearn.tree.DecisionTreeClassifier - sklearn.ensemble.RandomForestClassifier ١ whatever you want ؊ աইоࢲ, - sklearnਸ ਊ೧ AUCب ҅ ೧ࠇद. - ৈ۞ ݽ؛ٜ ࢿמਸ ࠺Ү ೧ ࠇद. - DecisionTreeClassifierܳ ࢎਊ೮؊ۄب, ࢎਊೠ featureо ܰݶ ӒѤ ܲ ݽ؛ੑפ . - ఋఋץ ݈Ҋ, ܲ classification ޙઁীب ഝਊ೧ ࠁࣁਃ.