Examples of using Logistic regression in English and their translations into Vietnamese
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more than two levels, multinomial logistic regression models were used.
like SVM and logistic regression, does not improve by a whole lot.
Softplus Multinomial logistic regression Dirichlet distribution- an alternative way to sample categorical distributions Smooth maximum.
Logistic regression analysis showed that the risk of severe fibrosis declined as caffeine and coffee consumption rose(p=0.023 for both).
In statistics, multinomial logistic regression is a classification method that generalizes logistic regression to multiclass problems, i.e. with more
So, for logistic regression, we define a different loss function that plays a similar role as that of the above loss function
This finding was not supported by a stepwise logistic regression analysis in which the following factors were identified as prognostic factors:
the model is learned, the predictions made by logistic regression can also be used as the probability of a given data instance belonging to class 0
Logistic Regression measures the relationship between the dependent variable(our label, what we want to predict) and the one
Reported that, as revealed through multivariable logistic regression, a higher in-hospital mortality rate was associated with older age
the model is learned, the predictions made by logistic regression can also be used as the probability of a given data instance belonging to class 0
It's sometimes feasible to estimate models for binary outcomes in datasets with just a little number of cases employing exact logistic regression.
the most common suspects are Naïve Bayes, random forests, logistic regression and, increasingly, neural networks.
It is sometimes possible to estimate models for binary outcomes in datasets with only a small number of cases using exact logistic regression(using the exlogistic command).
A reduction in the total MMSE score of 2 points over a 2-year period was associated with increased odds for developing dementia, according to the logistic regression analysis(odds ratio 3.55;
the researcher creates a statistical or machine learning model- for example, logistic regression- that predicts the human classification based on the features of the image.
she used features like“de Vaucouleurs fit axial ratio”- and her model was not logistic regression, it was an artificial neural network.
she used features like“de Vaucouleurs fit axial ratio”-and her model was not logistic regression, it was an artificial neural network.
Dr. Munger and team used conditional logistic regression to adjust for possible confounders, such as the year during which the blood sample was taken, the number of
LOGISTIC REGRESSION: It is a powerful statistical way of modeling a binomial outcome with one or more explanatory variables.