Confusion matrix for classification model dataitgirls3 Instructor Sunmi Yoon

૑դ दрী

https://towardsdatascience.com/understanding-confusion-matrix-a9ad42dcfd62 ઺ਃೠ Accuracy, Precision, Recall ଘ ফܳ ઺बਵ۽ ࢤп೮਺

https://towardsdatascience.com/understanding-confusion-matrix-a9ad42dcfd62 ੉৻ী Specificity, Fall-out rate ઑӘ ؊ ׮নೠ пب۽ 
 confusion matrix
 ܳ ߄ۄࠁӝ

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 о੿೮ਸ ٸ,

https://www.medcalc.org/manual/roc-curves.php Thresholdܳ ӓױ੸ਵ۽ ਋ஏ ੉ز दெࠇद׮. যڃ ੌ੉ ੌযաաਃ? Precision: Recall: Specificity: Fall-out:

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 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੉ Ҁ஖ח ৔৉ হ੉ ৮߷ೞѱ ܻ࠙ ؼ ٸ ROC ழ࠳ (feature੄ class ߸߹ מ۱੉ ৮߷) ROC ழ࠳о ઝ࢚ױী оө਎ࣻ۾ feature੄ class ߸߹ מ۱੉ જ׮Ҋ ೡ ࣻ ੓׮.

https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html#sphx-glr-auto-examples-model-selection-plot-roc-py ݽ؛ٜ߹۽ ROC ழ࠳ܳ Ӓ۰ࢲ ࢿמਸ ಣоೡ ࣻ ੓׮.
 ৈӝө૑ ೮঻ભ. ৘ܳ ٜযࢲ যڃ ૕߽ਸ ৘ஏೠ׮Ҋ ೡ ٸী, 
 ೞט࢝ ۄੋ਷ ആ঑ਸ histogram੄ 
 X୷ਵ۽ ೞח ݽ؛

https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html#sphx-glr-auto-examples-model-selection-plot-roc-py ݽ؛ٜ߹۽ ROC ழ࠳ܳ Ӓ۰ࢲ ࢿמਸ ಣоೡ ࣻ ੓׮.
 ৈӝө૑ ೮঻ભ. ৘ܳ ٜযࢲ যڃ ૕߽ਸ ৘ஏೠ׮Ҋ ೡ ٸী, 
 ೞט࢝ ۄੋ਷ ആ঑ਸ histogram੄ 
 X୷ਵ۽ ೞח ݽ؛ ઱ട࢝ ۄੋ਷ ఃܳ histogram੄ X୷ਵ۽ ೞח ݽ؛੉ۄҊ ೡ ٸী যڃ ݽ؛੄ ಌನݢझо જաਃ? = যڃ featureо ૕߽ী ؀ೠ ࢸݺ۱੉ ڪযա׮Ҋ ೡ ࣻ ੓աਃ?

য়ט਷ ੉Ѧ ݠन۞׬ ݽ؛ী ੸ਊ೧ ࢤп೧ ࠇद׮.

https://www.medcalc.org/manual/roc-curves.php ૑Әө૑੄ X ୷਷ ആ঑, ః ١ ૕߽ਸ оܰח feature ٜ੉঻णפ׮. X୷ਸ ަ۽ ࢶఖೞוջী ٮۄ, ف distribution਷ Ҁ஖ӝب ೞҊ ٮ۽ ڄযઉ ੓ӝب ೮૑ਃ.

https://www.medcalc.org/manual/roc-curves.php ૑Әࠗఠ X୷਷ Classification model੄ class probability۽ ೞѷणפ׮.

Sex <= 0.5 gini = 0.473 samples = 891 value = [549, 342] class = Survived Fare <= 26.269 gini = 0.306 samples = 577 value = [468, 109] class = Survived True Fare <= 48.2 gini = 0.383 samples = 314 value = [81, 233] class = Dead False gini = 0.226 samples = 415 value = [361, 54] class = Survived gini = 0.448 samples = 162 value = [107, 55] class = Survived gini = 0.447 samples = 225 value = [76, 149] class = Dead gini = 0.106 samples = 89 value = [5, 84] class = Dead ਋ܻо ૑Әө૑ न҃ॳ૑ ঋও؍, ੉ class ੿੄ ߑधਸ ߄Բח ѩפ׮.

Sex <= 0.5 gini = 0.473 samples = 891 value = [549, 342] class = Survived Fare <= 26.269 gini = 0.306 samples = 577 value = [468, 109] class = Survived True Fare <= 48.2 gini = 0.383 samples = 314 value = [81, 233] class = Dead False gini = 0.226 samples = 415 value = [361, 54] class = Survived gini = 0.448 samples = 162 value = [107, 55] class = Survived gini = 0.447 samples = 225 value = [76, 149] class = Dead gini = 0.106 samples = 89 value = [5, 84] class = Dead ഛܫ੄ ҙ੼ীࢲ ࠌਸ ٸ 1ߣ leafী ب଱ೠ ؘ੉ఠо ‘Survived’, ૊ ࢑ ࢎۈ੄ ؘ੉ఠੌ ഛܫ਷ ݻ ੑפө?

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https://www.medcalc.org/manual/roc-curves.php Predict Probability Survived Dead

૞ਖ਼. ૒੽ Ӓ۰ࠁওणפ׮.

Decision Tree੄ ӝࠄ ࣁ౴਷ Thresholdо 0.5ী ੓ભ. Leaf ֢٘ী җ߈ਵ۽ ౠ੿ ۄ߰ ؘ੉ఠо ٜয੓׮ݶ, Ӓ ۄ߰ਵ۽ ௿ېझܳ ੿೧ߡ݀פ׮.

Ӓۧ׮ݶ, ৈక ਋ܻо ߓ਍ ੌٜਸ ڙэ੉ ੸ਊ ೧ ࠅ ࣻ ੓ѷભ. Thresholdܳ ੉زदఃݶࢲ ROC ழ࠳ܳ Ӓܾ ࣻ ੓ѷભ?

ࢲ۽ ׮ܲ ݽ؛ٜ੄ Probability Predictionਸ ੉ਊೠ ൤झషӒ۔ਸ ࠁҊ ROC ழ࠳੄ ݽনө૑ ૗੘ ೧ ࠅ ࣻ ੓णפ׮. Decision Tree Features: ['Pclass', 'Sex', 'Family', 'C', 'Q', 'S'] Decision Tree Features: [‘Pclass', 'Family']

য়ט੄ ޷࣌ 1. ਤ੄ ݽ؛ࠁ׮ ࢿמ੉ જ਷ classification ݽ؛ਸ ٜ݅Ҋ, seaborn.displot ࢤӣ࢜ ࠺Үೞӝ 2. Thresholdܳ ਑૒ৈоݶࢲ ROC ழ࠳ ࢚ ઝ಴ܳ ݻ ѐ݅ ଺ইࠁӝ

׮਺ दрীח 1. ୓҅੸ਵ۽ Threshold ੉زೞݴ ROC ழ࠳ ৮ࢿೞӝ 2. sklearn.metrics.roc_curve ࢎਊೞৈ ৈ۞ ݽ؛ ࢿמ ࠺Үೞӝ 3. ୭੸੄ Threshold ଺ӝ

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