Translation of "Logistische regression" in English

Age remains significant in multiple logistic regression analyses.

This includes logistic regression, decision trees and neural networks.

Another commonly-used fitting technique is logistic regression .

Binary logistic regression classifies data into two groups.

Logistic regression is an advanced topic.

Logistic regression revealed a statistically significant association between nintedanib exposure and DCE-MRI response.

Logistic regression revealed a statistically significant association of the anti-angiogenic effect to nintedanib exposure.

This platform supports simple linear regression, logistic regression, ANOVA, ANOM and contingency analyses.

The logistic regression allows the estimation of the influence of pressure conditions on the so-called log-odds ratio.

If the variable is positive with low values and represents the repetition of the occurrence of an event, then count models like the Poisson regression or the negative binomial model may be used instead.

Issues discussed included: methodological problems in implementing the European TDI Protocol (see p.2); progress in establishing national information systems in the candidate countries; treatment-data findings both at European level and in specific countries (Spain, Netherlands, UK); and potential future developments in data analysis (e.g. logistic regression for drawing up clients' risk profiles).

The response of such a classifier is the result of the comparison of the continuous estimator to a threshold (see discriminant analysis and logistic regression).

Use Fit Y by X or Fit Model for nominal or ordinal logistic regression.

Through these four courses, you’ll learn about linear regression and logistic regression, and how you can use statistical data to create artificial neural networks.

Because, just like with a self-driving car, all AI systems have a data scientist who must make decisions about which modelling approaches to use — deep learning, random forest or logistic regression.

The features in Banerji and colleagues' machine learning model were more complex than those in my toy example—for example, she used features like "de Vaucouleurs fit axial ratio"—and her model was not logistic regression, it was an artificial neural network.

Logistic regression (also known as logit model) is often used for predictive analytics and modeling, extending to applications in machine learning.

Multivariate statistical analysis procedure, whose various forms were developed for different measurement and scale qualities of the variables that are to be analysed. (e.g. OLS or ordinary least squares regression for dependent variables at the metric measurement level, non-linear regression (e.g. logistic regression) for other forms of dependent variables).

In addition, stepwise regression, logistic regression (both nominal and ordinal) and discriminant analysis in JMP Pro support the use of a validation column.

In this case, Blumenstock used logistic regression with 10-fold cross-validation, but he could have used a variety of other statistical or machine learning approaches.

The method according to claim 1, wherein for determining signal type, a discriminant analysis or logistic regression is carried out based on beat-wise analysis of the patient pressure signals in time and frequency ranges.

The logistic regression or discriminant analysis uses both values from the time range and also characteristics of the Fourier transformation of the relevant beat.

The analytical method according to any one of claims 2 to 4, wherein the mathematical technique is a method using discriminant analysis, multiple regression analysis, principal components regression analysis, partial least squares regression, or logistic regression.

Classical statistical techniques like linear or logistic regression and linear discriminant analysis do not work well for high dimensional data (i.e. when the number of observations n is smaller than the number of features or predictors p: n < p).

A significantly lower tendency towards dual training on the part of those from a migrant background is also revealed in a multivariate analysis (logistic regression), in which characteristics relating to social origin (education of parents, occupational status of the father), prior school learning (school leaving qualification, school marks, type of school last attended) and the gender of the young people were taken into account at the same time (Beicht/Walden 2014).

Trends come and go, the logistic regression remains - find out in this blog post from our data science expert Christoph Hoffmann why!

This includes classical statistical methods (logistic and ordinal regression) and selected models from the field of Data Mining (decision trees).

Use Fit Y by X or Fit Model for nominal or ordinal logistic regression.

Given the data matrix and the desired output (e.g., whether the image was classified by a human as an elliptical galaxy), the researcher estimates the parameters of a statistical model—for example, something like a logistic regression—that predicts the human classification based on the features of the image.