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Advanced Analytics | Learn SAS | Machine Learning
SAS Korea 0
머신러닝 해석력 시리즈 4탄: 라임(LIME)으로 모델 해석력 개선하기!

그 동안 머신러닝 해석력 시리즈를 통해서 머신러닝의 부분 의존성(PD; Partial Dependence), 데이터 세트 해석 등을 소개해드렸는데요. 오늘은 라임(LIME; Local Interpretable Model-Agnostic Explanation)을 통해 머신러닝 모델의 해석력을 개선할 수 있는 방법에 대해서 알아보겠습니다. 머신러닝 모델 해석력 시리즈 1탄, 2탄, 3탄을 놓치셨다면 아래 링크를 통해 확인해주세요! 머신러닝 해석력 시리즈 1탄: 인공지능(AI)과 머신러닝을 신뢰하기 위한 필수

Data Visualization | Learn SAS | Programming Tips
Sanjay Matange 0
The SGPIE Procedure - Part 2

The SAS 9.4M6 software includes a new SGPIE procedure (preproduction) as introduced in the recent article - The SGPIE Procedure.  In that article, I described the basic features of the two statements supported in the procedure, the PIE and the DONUT, with some examples. It is my humble opinion that

Analytics | Learn SAS
Rick Wicklin 0
Fit the Pareto distribution in SAS

Will the real Pareto distribution please stand up? SAS supports three different distributions that are named "Pareto." The Wikipedia page for the Pareto distribution lists five different "Pareto" distributions, including the three that SAS supports. This article shows how to fit the two-parameter Pareto distribution in SAS and discusses the

Analytics | Learn SAS | Programming Tips
Steffen Stahl 0
Erstes SAS User Group Meetup: „Am besten gehen die User am nächsten Tag zur Arbeit und legen direkt los“

Vor ein paar Wochen hat das erste deutsche SAS User Group Meetup in Berlin stattgefunden. Dort hatte ich die Gelegenheit, mit Igor Khorlo, Senior Statistical Programmer bei Syneos Health Deutschland und Gründer der SAS User Group Germany (SUGG), über seine Motivation zu sprechen, die Gruppe ins Leben zu rufen –

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