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지난 'SAS 커스터머 인텔리전스 360(SAS Customer Intelligence 360): 머신러닝의 블랙 박스 모델이란’ 블로그에서 머신러닝 모델은 다면적이고 계속 진화하는 주제라고 소개해드린 바 있는데요. 오늘은 머신러닝 모델의 해석력(Interpretability)에 대해 자세히 살펴보고자 합니다. 머신러닝 모델은 놀라운 예측 능력을 제공하지만 매우 복잡하여 이해하기 쉽지 않습니다. 또한 머신러닝 모델은 예측한 결과에 대한 명확한 설명도 제공하지 않기
The 6th maintenance release of SAS 9.4 is now available for users. Along with it comes a new SG procedure - the SGPIE procedure (pre-production). The primary audience for the SG procedures has been the analytical user, for creating effective displays of analytical data. However, the rich feature set of these
Do you remember The Matrix movies, that started coming out in 1999? Hopefully this movie franchise didn't give you a fear of virtual reality and AI. The thing I remember most from the movie was the really cool slow-motion video effects (from multiple angles) in the fight scenes. And the
As analíticas vão ser imprescindíveis no futuro. Está é a opinião generalizada da maioria dos gestores. No entanto um estudo levado a cabo pelo SAS sobre Plataforma Analítica - “Here and Now: the need for an analytics platform”, revelou que apenas 35% está efectivamente com projectos de implementação ao nível
Joseph Woodside discusses the evolution of digital transformation in healthcare in three eras.
Today, we’re talking about the Match.com of the corporate world: LinkedIn. Love it or hate it, LinkedIn is essential for making professional connections and, ultimately, getting hired. According to a CareerBuilder survey, 70% of recruiters say they use social media channels to research candidates online. And what you have on
Last week my colleague, Robert Allison, visualized data regarding immunization rates for kindergarten classes in North Carolina. One of his graphs was a scatter plot that displayed the proportion of unimmunized students versus the size of the class for 1,885 kindergarten classes in NC. This scatter plot is the basis
Está próximo a celebrarse en México (el 27 de noviembre) Hot Sale, evento anual cuyo propósito es impulsar las ventas de productos de consumo en línea a través de ofertas, descuentos y promociones especiales. Organizado por la Asociación Mexicana de Ventas en Línea (AMVO), la Asociación Mexicana de Internet (AMIPCI)
Hace unas semanas se celebró la vigésima sexta edición de Góndola, el encuentro más importante para la industria del retail en Colombia. En su desarrollo se habló de los cambios que ha venido enfrentando el sector debido a la variedad de los perfiles de los nuevos clientes, y se afirmó que
Blockchain es una tecnología que tradicionalmente se ha asociado a la industria financiera por ser la base que permitió la creación y consolidación del Bitcoin, la famosa criptomoneda. A grandes rasgos, Blockchain es una base de datos distribuida y segura (gracias al cifrado), utilizada para almacenar todo tipo de transacciones,
만약 나의 주치의가 내 디지털 트윈(Digital Twin)을 만들어 실시간으로 나의 상황을 다양한 센서 데이터 등을 통해 업데이트 받을 수 있다면 어떨까요? 현실세계의 기계나 장비, 사물 등을 컴퓨터 속 가상세계에 구현한 디지털 트윈을 통해 몸 속의 잠재적인 질병에 대한 신호를 미리 받을 수 있을지도 모릅니다. 디지털 트윈이 암 관련 질병을 미리
PythonからSAS Viyaの機能を利用するための基本パッケージであるSWATと、よりハイレベルなPython向けAPIパッケージであるDLPyを使用して、Jupyter NotebookからPythonでSAS Viyaの機能を使用して一般物体検出（Object Detection）を試してみました。 今回は、弊社で用意した数枚の画像データを使用して、処理の流れを確認するだけなので、精度に関しては度外視です。 大まかな処理の流れは以下の通りです。 １．必要なパッケージ（ライブラリ）のインポートとセッションの作成 ２．一般物体検出向け学習用データの作成 ３．モデル構造の定義 ４．モデル生成（学習） ５．物体検出（スコアリング） １．必要なパッケージ（ライブラリ）のインポートとセッションの作成 swatやdlpyなど、必要なパッケージをインポートします。 from swat import * import sys sys.path.append(dlpy_path) from dlpy.model import * from dlpy.layers import * from dlpy.applications import * from dlpy.utils import * from dlpy.images import ImageTable from dlpy.splitting import two_way_split from dlpy.blocks import *
This week I noticed that they've started building the lot where they sell Christmas trees near SAS (at the intersection of Maynard & Reedy Creek Rd). They put up a nice rustic wooden fence, and lights, and maybe even a fire pit to keep their workers warm. They sell some
Burger and fries, wine and cheese, peanut butter and jelly … some things just go better together. For organizations embarking on digital transformation, AI and IoT just go better together. These two distinct technologies; AI and IoT (or AIoT) are a natural fit. To take an analogy from the human
A data analyst asked how to compute parameter estimates in a linear regression model when the underlying data matrix is rank deficient. This situation can occur if one of the variables in the regression is a linear combination of other variables. It also occurs when you use the GLM parameterization