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	<title>Computational Prediction</title>
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		<title>L1 norm VS L2 norm</title>
		<link>http://mkseo.pe.kr/stats/?p=1282&#038;utm_source=rss&#038;utm_medium=rss&#038;utm_campaign=l1-norm-vs-l2-norm</link>
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		<dc:creator><![CDATA[Minkoo Seo]]></dc:creator>
		<pubDate>Thu, 16 Feb 2023 06:14:37 +0000</pubDate>
				<category><![CDATA[Uncategorized]]></category>
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					<description><![CDATA[https://stackoverflow.com/questions/32276391/feature-normalization-advantage-of-l2-normalization p-norm 정의]]></description>
		
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		<title>lightgbm</title>
		<link>http://mkseo.pe.kr/stats/?p=1270&#038;utm_source=rss&#038;utm_medium=rss&#038;utm_campaign=lightgbm</link>
					<comments>http://mkseo.pe.kr/stats/?p=1270#respond</comments>
		
		<dc:creator><![CDATA[Minkoo Seo]]></dc:creator>
		<pubDate>Sat, 28 Jan 2023 04:51:36 +0000</pubDate>
				<category><![CDATA[Uncategorized]]></category>
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					<description><![CDATA[xgboost vs lightgbm 검색해보니 이렇게 훌륭한 글을 올려두신 분이 계시네요. https://assaeunji.github.io/machine%20learning/2021-01-07-xgboost/ 읽어보니 둘간에 사용하는 방법은 크게 다르지 않아서 xgboost 코드를 거의 그대로 쓸 수 있어 보입니다.]]></description>
		
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		<title>Deep learning model interpretation</title>
		<link>http://mkseo.pe.kr/stats/?p=1267&#038;utm_source=rss&#038;utm_medium=rss&#038;utm_campaign=deep-learning-model-interpretation</link>
					<comments>http://mkseo.pe.kr/stats/?p=1267#respond</comments>
		
		<dc:creator><![CDATA[Minkoo Seo]]></dc:creator>
		<pubDate>Wed, 25 Jan 2023 11:08:50 +0000</pubDate>
				<category><![CDATA[Uncategorized]]></category>
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					<description><![CDATA[SHAP 란 &#8220;SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model.&#8221; 이라고 합니다. 사용 사례는 https://walkwithfastai.com/SHAP 에서 볼 수 있습니다.]]></description>
		
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		<title>Deep learning model tuning</title>
		<link>http://mkseo.pe.kr/stats/?p=1265&#038;utm_source=rss&#038;utm_medium=rss&#038;utm_campaign=deep-learning-model-tuning</link>
					<comments>http://mkseo.pe.kr/stats/?p=1265#respond</comments>
		
		<dc:creator><![CDATA[Minkoo Seo]]></dc:creator>
		<pubDate>Wed, 25 Jan 2023 11:06:34 +0000</pubDate>
				<category><![CDATA[Uncategorized]]></category>
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					<description><![CDATA[구글 리서치에서 나온 딥 러닝 튜닝 가이드 https://github.com/google-research/tuning_playbook]]></description>
		
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		<title>딥러닝 파라미터 튜닝</title>
		<link>http://mkseo.pe.kr/stats/?p=1263&#038;utm_source=rss&#038;utm_medium=rss&#038;utm_campaign=%25eb%2594%25a5%25eb%259f%25ac%25eb%258b%259d-%25ed%258c%258c%25eb%259d%25bc%25eb%25af%25b8%25ed%2584%25b0-%25ed%258a%259c%25eb%258b%259d</link>
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		<dc:creator><![CDATA[Minkoo Seo]]></dc:creator>
		<pubDate>Fri, 20 Jan 2023 16:24:18 +0000</pubDate>
				<category><![CDATA[Uncategorized]]></category>
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					<description><![CDATA[늘 뭔가 흑마술처럼 생각되는게 파라미터 튜닝인데 구글에서 좋은 글을 github에 올렸습니다. https://github.com/google-research/tuning_playbook 이전까지는 그냥 남들 쓰는 파라미터 배끼고 남들 쓰는 범위안에서 GridSearchCV 돌리는게 보통이었는데 참고해볼만 할 것 같습니다.]]></description>
		
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		<title>제안을 위한 분석</title>
		<link>http://mkseo.pe.kr/stats/?p=1260&#038;utm_source=rss&#038;utm_medium=rss&#038;utm_campaign=%25ec%25a0%259c%25ec%2595%2588%25ec%259d%2584-%25ec%259c%2584%25ed%2595%259c-%25eb%25b6%2584%25ec%2584%259d</link>
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		<dc:creator><![CDATA[Minkoo Seo]]></dc:creator>
		<pubDate>Mon, 16 Jan 2023 10:34:45 +0000</pubDate>
				<category><![CDATA[Uncategorized]]></category>
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					<description><![CDATA[일하다보면 종종 데이터 탐색과 그 탐색의 결과로 제안을 하는 일을 구분하지 못하는 경우를 본다. 문서를 쓰면서 어떤 분석을 했는지 raw data를 하나하나 나열하고 그 과정의 어려움을 설명하고, 남아있는 기술적 문제를 설명하려 하던가. 이런 설명이 필요한 컨텍스트와 리더쉽 또는 product manager 에게 자신의 분석을 보이는 설명은 서로 달라야한다. 내 분석을 통해 설득하고 제안하는 것이 주가 되어야한다. [&#8230;]]]></description>
		
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		<title>Imbalanced data를 다루는 라이브러리</title>
		<link>http://mkseo.pe.kr/stats/?p=1258&#038;utm_source=rss&#038;utm_medium=rss&#038;utm_campaign=imbalanced-data%25eb%25a5%25bc-%25eb%258b%25a4%25eb%25a3%25a8%25eb%258a%2594-%25eb%259d%25bc%25ec%259d%25b4%25eb%25b8%258c%25eb%259f%25ac%25eb%25a6%25ac</link>
					<comments>http://mkseo.pe.kr/stats/?p=1258#respond</comments>
		
		<dc:creator><![CDATA[Minkoo Seo]]></dc:creator>
		<pubDate>Sun, 15 Jan 2023 14:31:35 +0000</pubDate>
				<category><![CDATA[Uncategorized]]></category>
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					<description><![CDATA[Imbalanced data 를 다루는데 유용한 Imbalanced learn이라는 라이브러리를 하나 찾았습니다. Scikit learn 도 그렇듯이 이런 라이브러리의 장점은 메뉴얼만 보고 있어도 어떤 알고리즘들이 존재하는지를 쉽게 알 수 있단 점입니다. 특히 undersampling technique 방법이라고는 random sampling 만 생각하고 있다가 다양한 Prototype selection 알고리즘을 접하게 되었는데 이게 참 인상적이네요. 예를들어 Near Miss 1, 2, 3 알고리즘이 인상깊었습니다. 단순히 [&#8230;]]]></description>
		
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		<title>Decision tree가 가진 설명력의 한계</title>
		<link>http://mkseo.pe.kr/stats/?p=1249&#038;utm_source=rss&#038;utm_medium=rss&#038;utm_campaign=decision-tree%25ea%25b0%2580-%25ea%25b0%2580%25ec%25a7%2584-%25ec%2584%25a4%25eb%25aa%2585%25eb%25a0%25a5%25ec%259d%2598-%25ed%2595%259c%25ea%25b3%2584</link>
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		<dc:creator><![CDATA[Minkoo Seo]]></dc:creator>
		<pubDate>Tue, 01 May 2018 07:43:46 +0000</pubDate>
				<category><![CDATA[Uncategorized]]></category>
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					<description><![CDATA[Decision tree는 흔히 설명력이 좋다고 한다. 트리를 보면 어떻게 분류가 되는지 볼 수 있기에 그렇다. 하지만 실제로 모델을 적용하는데는 한계가 발생하는 경우가 있다. 예를들어 X[i]=0 이라면 logistic regression의 경우 Coef[i] * X[i] = 0 이 되어 X[i]가 예측에 영향을 주지 않는다. 하지만 Decision tree는 X[i]=0 인 경우에 어떤 결론을 내릴 수 있다. 예를들어 영어 문장내 [&#8230;]]]></description>
		
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		<title>Interpreting Random forest</title>
		<link>http://mkseo.pe.kr/stats/?p=1246&#038;utm_source=rss&#038;utm_medium=rss&#038;utm_campaign=interpreting-random-forest</link>
					<comments>http://mkseo.pe.kr/stats/?p=1246#respond</comments>
		
		<dc:creator><![CDATA[Minkoo Seo]]></dc:creator>
		<pubDate>Mon, 23 Apr 2018 09:59:12 +0000</pubDate>
				<category><![CDATA[Uncategorized]]></category>
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					<description><![CDATA[http://blog.datadive.net/interpreting-random-forests/ This is how eli5 explains a tree.]]></description>
		
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		<title>ZCA Whitening</title>
		<link>http://mkseo.pe.kr/stats/?p=1238&#038;utm_source=rss&#038;utm_medium=rss&#038;utm_campaign=zca-whitening</link>
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		<dc:creator><![CDATA[Minkoo Seo]]></dc:creator>
		<pubDate>Thu, 02 Feb 2017 14:07:56 +0000</pubDate>
				<category><![CDATA[Uncategorized]]></category>
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					<description><![CDATA[Stack exchange 글인데 정말 훌륭한 설명입니다. 이렇게 핵심만 전달하는 능력이 있다니.. What is the difference between ZCA whitening and PCA whitening? 같은 사람의 PCA를 사용한 whitening 방법에 대한 글도 훌륭하네요.]]></description>
		
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