Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Sign up for free
Menu
Search
Features
All features
Private URLs
Password Protection
Custom URLS
Scheduled publishing
Remove Branding
Restrict embedding
Deck Collections
Notes
Features
All features
Private URLs
Password Protection
Custom URLS
Scheduled publishing
Remove Branding
Restrict embedding
Deck Collections
Notes
Explore
Featured decks
Featured speakers
Programming
Technology
Storyboards
Explore
Featured decks
Featured speakers
Programming
Technology
Storyboards
Pricing
Search
Sign in
Sign up for free
Confusion matrix
Search
Sponsored
·
Your Podcast. Everywhere. Effortlessly.
Share. Educate. Inspire. Entertain. You do you. We'll handle the rest.
→
Sunmi Yoon
November 03, 2019
Technology
190
0
Share
Embed
Copy iframe code
Copy JS code
Copy link
Start on current slide
Confusion matrix
Confusion matrix 기초부터 머신러닝 응용까지 for dataitgirls3
Sunmi Yoon
November 03, 2019
More Decks by Sunmi Yoon
See All by Sunmi Yoon
데이터 분석가 채용 공고 읽는 방법
ysunmi0427
1
410
Deep down in classification 0.5 magic number
ysunmi0427
0
120
Tree Methods
ysunmi0427
0
150
심슨의 역설
ysunmi0427
0
2.6k
회사는 어떤 사람을 데이터 분석가로 채용하고 싶어하는 것일까?
ysunmi0427
0
2.7k
Other Decks in Technology
See All in Technology
V8のArray.prototype.flatを最大約20倍速くするまでと、巨大OSSへの大規模コミットの道のり
riyaamemiya
0
230
全人類(ほぼ)AWS Organizations の上でAWSを利用している、その世界を知る話
htan
1
130
AIは爆速なのに、私が詰まっていた話 ― 音声入力と鳴くマスコットでボトルネックを削る
yama3133
0
600
Kiro Crewをはじめよう
kizawa2020
1
140
AIに攻撃される前に、AIに攻撃させる
tsuchikazu
0
170
AgentCoreで実践するハーネスエンジニアリング
yakumo
1
300
事業活動を AI Ready にする攻めと守りのデータエンジニアリング / data-engineering-for-ai-ready-business
pei0804
7
2.5k
継続的なAIコスト最適化
bgpat
1
280
組み立てて楽しむ AWS Blocks 入門
kmiya84377
0
230
OR学会2026年秋期大会_機械学習と数理最適化の融合による、配合レシピ最適化システム開発事例
brainpadpr
0
130
USB PDを使ってスマホを労わりたい
raspython3
0
160
並行性の問題を防げ!実践トランザクション入門
occhi
0
280
Featured
See All Featured
Faster Mobile Websites
deanohume
310
32k
JAMstack: Web Apps at Ludicrous Speed - All Things Open 2022
reverentgeek
1
630
Stop Working from a Prison Cell
hatefulcrawdad
274
21k
What the history of the web can teach us about the future of AI
inesmontani
PRO
2
730
How to make the Groovebox
asonas
2
2.5k
SEO in 2025: How to Prepare for the Future of Search
ipullrank
3
3.9k
Have SEOs Ruined the Internet? - User Awareness of SEO in 2025
akashhashmi
0
520
Dominate Local Search Results - an insider guide to GBP, reviews, and Local SEO
greggifford
PRO
0
360
Bootstrapping a Software Product
garrettdimon
PRO
306
120k
Organizational Design Perspectives: An Ontology of Organizational Design Elements
kimpetersen
PRO
1
840
Become a Pro
speakerdeck
PRO
31
6.3k
Design of three-dimensional binary manipulators for pick-and-place task avoiding obstacles (IECON2024)
konakalab
0
610
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.