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
330
Deep down in classification 0.5 magic number
ysunmi0427
0
92
Tree Methods
ysunmi0427
0
120
심슨의 역설
ysunmi0427
0
2.2k
회사는 어떤 사람을 데이터 분석가로 채용하고 싶어하는 것일까?
ysunmi0427
0
2.3k
Other Decks in Technology
See All in Technology
バクラクによるコーポレート業務の自動運転 #BetAIDay
layerx
PRO
1
910
GMOペパボのデータ基盤とデータ活用の現在地 / Current State of GMO Pepabo's Data Infrastructure and Data Utilization
zaimy
3
210
Claude CodeでKiroの仕様駆動開発を実現させるには...
gotalab555
3
970
dipにおけるSRE変革の軌跡
dip_tech
PRO
1
250
「Roblox」の開発環境とその効率化 ~DAU9700万人超の巨大プラットフォームの開発 事始め~
keitatanji
0
120
AIに目を奪われすぎて、周りの困っている人間が見えなくなっていませんか?
cap120
1
530
Amazon S3 Vectorsは大規模ベクトル検索を低コスト化するサーバーレスなベクトルデータベースだ #jawsugsaga / S3 Vectors As A Serverless Vector Database
quiver
1
170
AI関数が早くなったので試してみよう
kumakura
0
230
20250807_Kiroと私の反省会
riz3f7
0
200
人に寄り添うAIエージェントとアーキテクチャ #BetAIDay
layerx
PRO
9
2.1k
JAWS AI/ML #30 AI コーディング IDE "Kiro" を触ってみよう
inariku
3
350
Eval-Centric AI: Agent 開発におけるベストプラクティスの探求
asei
0
110
Featured
See All Featured
Imperfection Machines: The Place of Print at Facebook
scottboms
267
13k
The MySQL Ecosystem @ GitHub 2015
samlambert
251
13k
Speed Design
sergeychernyshev
32
1.1k
Understanding Cognitive Biases in Performance Measurement
bluesmoon
29
1.8k
[RailsConf 2023] Rails as a piece of cake
palkan
56
5.7k
Done Done
chrislema
185
16k
Designing Dashboards & Data Visualisations in Web Apps
destraynor
231
53k
Making Projects Easy
brettharned
117
6.3k
A Modern Web Designer's Workflow
chriscoyier
695
190k
Refactoring Trust on Your Teams (GOTO; Chicago 2020)
rmw
34
3.1k
What's in a price? How to price your products and services
michaelherold
246
12k
Being A Developer After 40
akosma
90
590k
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.