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Tree Methods
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Sunmi Yoon
November 04, 2019
Technology
0
120
Tree Methods
Decision Tree, Random Forest를 dataitgirls3 학생들에게 가르치기 위해 만든 수업자료입니다.
Sunmi Yoon
November 04, 2019
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Transcript
Tree methods dataitgirls3 Instructor Sunmi Yoon
Decision Tree
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
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 Root Node (ࡸܻ) Intermediate Node (о) Terminal Node, Leaf ()
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 അ ਤী ؘఠо ݻ ѐ ਤ೧ ח Ӓ ؘఠٜ যڃ ۄ߰ਸ оҊ ח
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 যڃ ӝળਵ۽ оӝܳ ೮ח (gini ژח entropy)
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 Terminal Nodeী بೠ ؘఠٜਸ যڌѱ ࠙ܨೡ Ѫੋ
sklearn Code
Impurity
Impurity ࢎѾաޖח Impurity (ࠛࣽب, ࠛഛपࢿ) ծইח ߑߨਵ۽ णפ. ࣽبо ૐоೞח
Ѫਸ فҊ Information gainۄҊ ೞӝب פ. য়ט ࢎѾաޖ ࠛࣽب ஏ ߑߨ , Gini Indexܳ ҕࠗפ.
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 G = d ∑ i=1 Ri ( 1 − m ∑ k=1 p2 ik) Step 1. gini = 0.473 ਸ ҅೧ যࠁࣁਃ Step 2. gini = 0.226 ਸ ҅೧ যࠁࣁਃ
https://imgur.com/n3MVwHW
Random Forest
ৈ۞ ܻٜਸ ‘ܰѱ’ ݅ٚ. https://www.researchgate.net/figure/Architecture-of-the-random-forest-model_fig1_301638643
https://community.alteryx.com/t5/Alteryx-Designer-Knowledge-Base/Seeing-the-Forest-for-the-Trees-An-Introduction-to-Random-Forest/ta-p/158062 bagging = bootstrap aggregating
Bagging ߓӦ(bagging) bootstrap aggregating ড۽, ࠗझە(bootstrap)ਸ ా೧ ઑӘঀ ܲ ള۲
ؘఠী ೧ ള۲ػ ӝୡ ࠙ܨӝ(base learner)ٜਸ Ѿ(aggregating)दఃח ߑߨ. ࠗझەۆ, য ള۲ ؘఠীࢲ ࠂਸ ೲਊೞৈ ਗ ؘఠࣇҗ э ӝ ؘఠࣇਸ ݅٘ח җਸ ݈ೠ. ߓӦਸ ా೧ ےؒ ನۨझܳ ള۲दఃח җ җ э ࣁ ױ҅۽ ೯ػ. 1. ࠗझە ߑߨਸ ా೧ Nѐ ള۲ ؘఠࣇਸ ࢤࢿೠ. 2. Nѐ ӝୡ ࠙ܨӝ(ܻ)ٜਸ ള۲दఅ. 3. ӝୡ ࠙ܨӝ(ܻ)ٜਸ ೞա ࠙ܨӝ(ےؒ ನۨझ)۽ Ѿೠ(ಣӐ ژח җ߈ࣻై ߑध ਊ). Wikipedia ےؒನۨझ > ߓӦਸ ਊೠ ನۨझ ҳࢿ
sklearn Code