Examples of using Logistic regression in English and their translations into Vietnamese
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Linear and Logistic Regression.
The first model we considered was the logistic regression.
Ordered logistic regression is used when the dependent variable is ordered, but not continuous.
Prediction with logistic regression.
When the dependent variable has more than two categories,then it is a multinomial logistic regression.
Predicting with Logistic Regression.
If there are more than 2 classes in thetarget variable than it is known as Multinomial Logistic Regression.
It's called logistic regression.
Familiarize yourself with common formulas such as Bayes Theorem andthe derivation of popular models such as logistic regression and SVM.
This is called logistic regression.
Logistic regression is a statistical method for analyzing a dataset in which there are one or more independent variables that determine an outcome.
This is known as logistic regression.
It's sometimes feasible to estimate models for binary outcomes in datasets with just alittle number of cases employing exact logistic regression.
You might want to learn logistic regression.
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.
This tends to be known as Logistic regression.
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.
Classification and it's called logistic regression.
Logit- also known as logistic regression- is more popular in health sciences like epidemiology partly because coefficients can be interpreted in terms of odds ratios.
The Log Odds Chart is central to the Logistic Regression Model.
A factorial logistic regression is used when you have two or more categorical independent variables but a dichotomous dependent variable.
For discrete outcome measures with more than two levels, multinomial logistic regression models were used.
The goal of logistic regression is to use the training data to find the values of coefficients such that it will minimize the error between the predicted outcome and the actual outcome.
As the amount of data increases, the performance of traditional learning algorithms,like SVM and logistic regression, does not improve by a whole lot.
So, for logistic regression, we define a different loss function that plays a similar role as that of the above loss function and also solves the optimization problem by giving a convex function.
This takes a number of health factors from a patient andusing precalculated logistic regression coefficients attempts to give a percentage chance of survival to discharge.
In statistics, multinomial logistic regression is a classification method that generalizes logistic regression to multiclass problems, i.e. with more than two possible discrete outcomes.
Because of the way that 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 or class 1.
Reported that, as revealed through multivariable logistic regression, a higher in-hospital mortality rate was associated with older age and catecholamine use(with or without steroids).