Examples of using Logistic regression in English and their translations into Ukrainian
{-}
-
Colloquial
-
Ecclesiastic
-
Computer
Logistic regression.
Linear and Logistic Regression.
Logistic regression.
Linear and Logistic Regression.
Classification(logistic regression).
Popular loss functions include the hinge loss(for linear SVMs)and the log loss(for linear logistic regression).
In these circumstances logistic regression is often used.
Keywords: intracranial aneurism hemorrhages, prognosis, logistic regression.
Despite its name, Logistic Regression is actually a classification algorithm.
Why do some formulas have the coefficient in the front in logistic regression likelihood, and some don't?
Techniques such as logistic regression and probit regression can be used for empirical analysis of discrete choice.
If a dependent variable in a regression is dichotomous,then logistic regression or probit regression is employed.
From this perspective, SVM is closely related to other fundamentalclassification algorithms such as regularized least-squares and logistic regression.
Next, build a probabilistic model(say, a logistic regression) based on these variables to predict whether a user will start drinking Soylent or not.
To reveal independent predictors of unfavorablecourse of the disease backward stepwise binary logistic regression analysis was used.
These include logistic regression, Poisson regression, analysis of'event history' data, and the Cox proportional hazards regression model.
Obtain an in-depth understanding of supervised andunsupervised learning models such as linear regression, logistic regression, SVM, clustering and K-NN.
Logistic regression analysis revealed that actigraphy- assessed shorter sleep duration was associated with an increased likelihood of development of a clinical cold.
The ProGERD study, likely the largest of its kind(gt;5,000 patients)used logistic regression analysis to identify several independent risk factors for ERD.
The special case of linear support-vector machines can be solved more efficiently by the same kind ofalgorithms used to optimize its close cousin, logistic regression;
In this case, Blumenstock used logistic regression, but he could have used a variety of other statistical or machine learning approaches.
There are many choices concerning learning algorithms, but the most common suspects are Naïve Bayes,random forests, logistic regression and, increasingly, neural networks.
Use of these independent determinants in a logistic regression model allows predicting unfavorable as to cardiovascular risk reduction in PPA< 130% with accuracy of 72.8%.
Risk factors of coronary atherosclerosis in patients with tortuouscoronary arteries were identified using logistic regression and discriminant analysis methods.
Some classification models, such as naive Bayes, logistic regression and multilayer perceptrons(when trained under an appropriate loss function) are naturally probabilistic.
Logistic regression- maximum likelihood estimation of w→{\displaystyle{\vec{w}}} assuming that the observed training set was generated by a binomial model that depends on the output of the classifier.
The softmax function is used in various multiclass classification methods,such as multinomial logistic regression(also known as softmax regression), multiclass linear discriminant analysis, naive Bayes classifiers, and artificial neural networks.
Logistic regression analysis showed that"a diagnosis of Parkinson's disease was not associated with the presence of symptoms of impulse control or related behavior, either individually or as a group," the authors reported.
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.