Examples of using Logistic regression in English and their translations into German
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In this case, Blumenstock used logistic regression, but he could have used a variety of other statistical or machine learning approaches.
Logistic regression(also known as logit model)
In this case, Blumenstock used logistic regression with 10-fold cross-validation,
Through these four courses, you will learn about linear regression  and logistic regression, and how you can use statistical data to create artificial neural networks.
a multi-level binary logistic regression can be deployed cf. Figure 5.
Using binary logistic regression, build models in which the dependent variable is dichotomous;
You could fit a logistic regression to this data, and then you could use the resulting model parameters to predict whether new students are going to graduate from college.
are analyzed using logistic regression in terms of their predictive skills for hail.
And(D) were from multivariable logistic regression smoothed by restricted cubic spline with three degrees and knots at 10th, 50th, and 90th percentiles of given exposure variable.
Pro contains mainstream methods such as descriptive statistics, linear or logistic regression, classification trees
and simple logistic regression.
In the first study model a simple logistic regression is performed which examines whether the chances of a company concluding a training contract with lower secondary school graduates increase or decrease.
linear and non-linear regression,  logistic regression, etc.
random forest or logistic regression.
our focus is on statistical methods designed specifically for the analysis of categorical variables like logistic regression, latent class analysis or cluster analysis.
neural networks, and logistic regression models for the SME identified features, as well as convolutional neural networks and recurrent neural networks for the deep learning case.
she used features like"de Vaucouleurs fit axial ratio"-and her model was not logistic regression, it was an artificial neural network.
They used conditional logistic regression to adjust for potential'polluters',
she used features like"de Vaucouleurs fit axial ratio"-and her model was not logistic regression, it was an artificial neural network.
Logistic regressions were additionally calculated in order to ascertain which aspects are still significant when all potential factors are taken into consideration at the same time.