Examples of using Logistic regression in English and their translations into Korean
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Linear and Logistic Regression.
선형 및 로지스틱 회귀.Logistic regression is an advanced topic.
로지스틱 회귀는 심화 주제입니다.Question about logistic regression.
Logistic regression 에 대하여 질문 올립니다.The Logistic Regression Cost Function.
Logistic 회귀의 cost function.Category Archives: Logistic Regression.
범주형 자료분석: Logistic Regression.The logistic regression model.
로지스틱 회귀(Logistic Regression) 모델의 구현.The answer to these problems is Logistic regression.
이런 문제를 해결하려 하는 것이 Logistic regression이다.Binary Logistic Regression".
Binary logistic regression(이항 로지스틱 회귀분석).This terminology is applied to both linear and logistic regression.
이 terminology들은 linear, logistic regression에 모두 적용된다.Logistic regression analysis was performed.
Logistic regression analysis를 시행하였다.Using kernels with logistic regression is going too very slow.
Logistic regression 에 kernel 을 사용할 수도 있겠지만, 상당히 느리다.Logistic regression analysis were performed.
Logistic regression analysis를 시행하였다.Predictive Modeling Using Logistic Regression(With SAS).
로지스틱 회귀를 이용한 예측 모델링 (Predictive Modeling Using Logistic Regression).이항 로지스틱 회귀분석 (Binary Logistic Regression).Chi-square test and Binary logistic regression analysis were used.
프로그램을 이용하여 Chi-square test와 Binary logistic regression analysis를 실시하였다.Logistic regression is often used to answer clearly defined yes or no questions.
Logistic Regression은 보통 Yes or No 문제를 정의할 때 사용한다.This shows that softmax regression is a generalization of logistic regression.
Softmax Multivariate logistic regression was used to predict the outcome.
Logistic regression은 원래 투표 결과를 예측하는데 많이 사용됩니다.In particular the key differences between these two models can be seen in the following two features of logistic regression.
특히, 이 두 모델의 주요 차이점은 로지스틱 회귀의 다음 두 가지 특징에서 볼 수 있습니다.Conditional logistic regression models were used.
통계분석은 조건부 로지스틱 회귀분석(conditional logistic regression models)을 사용하였다.The SAS Enterprise Guide task list shows that LOGISTIC is supported by(surprise!) the Logistic Regression task.
SAS Enterprise Guide작업 리스트는 LOGISTIC이 Logistic Regression 작업을 지원해준다고 보여준다.Using logistic regression to segment incoming customers by risk.
로지스틱 회귀를 사용하여 위험을 기준으로 들어오는 고객을 구분합니다. Using logistic regression to segment incoming customers by risk.This platform supports simple linear regression, logistic regression, ANOVA, ANOM and contingency analyses.
이 플랫폼은 단순 선형 Tip: if you're interested in taking your skills with linear regression to the next level, consider also DataCamp's Multiple and Logistic Regression course!
팁: 선형 회귀기술을 다음 단계로 끌어올리는 데 관심이 있다면 DataCamp의 다중 및 로지스틱 회귀 코스도 고려해 보십시오!We already have linear regression and we have logistic regression, so why do we need, you know, neural networks?
우리는 이미 Linear Regression이랑 Logistic regression을 배웠는데 (게다가 교수님이 우리가 많이 안다고 하셨는데) 왜 신경망 알고리즘을 알아야 할까?You can also build other models, such as MANOVA, repeated measures,generalized linear, loglinear variance or logistic regression(nominal and ordinal).
또한 MANOVA, 반복 측정 모형, 일반화 선형 모형,로그 선형 분산 모형, 로지스틱 회귀 모형(명목 및 순서) 등 다른 모형도 구축할 수 있습니다.If you open the Logistic Regression task and hunt around for a bit, you will find the Wald option within the Conditional odds ratios grouping on the Model: Options page.
당신이 Logistic Regression 작업을 열고 주변을 살펴보면, 모델을 그룹화한 조건부 오즈비를 포함하는 Wald 옵션을 찾을 것이다.: Options page.Statistical/Graphical Tools Used: Bar charts and frequency distributions, mosaic plots, contingency tables(cross tabs),chi-squared tests, logistic regression, predicted values and confusion matrix.
사용된 통계/그래픽 도구: 막대 차트 및 빈도 분포, 모자이크 도표, 우발성 테이블(교차 탭),카이 제곱 검정, 로지스틱 회귀, 예측값, 오차 행렬.Explain the principles of the following statistical analysis techniques: Logistic regression analysis, Poisson regression analysis, Analysis of event history data, including the Cox proportional hazards regression model.
다음 통계 분석 기술의 원리를 설명하십시오: Logistic 회귀 분석, Poisson Albert, A. and Anderson,J. A.(1984),“On the Existence of Maximum Likelihood Estimates in Logistic Regression Models,” Biometrika, 71, 1-10.
분리에 대한 자세한 내용은 Albert and J. A. Anderson (1984)"On the existence of maximum likelihood estimates in logistic regression models" Biometrika 71, 1, 1-10을 참조하십시오.
