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Usual recommendations concerning the practical implementation of regression analysis methods remain helpful in this context [ 41, 42].
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Whereas a direct implementation of ridge regression in this setting requires calculations of order O np2+p3), their algorithm uses only calculations of order O np2).
In the implementation of softmax regression, (lambda ) = 0.001 was used.
We can easily have a parallel implementation of our regression filter [1, 28] for efficient processing of a large number of noisy images.
After implementation of these regression models in both organs, 95% CIs were successfully calculated for each tumour size and for limits of PD and PR.
In benchmark tests among different tools [106], Mahout's implementation of Logistic Regression with SGD was left out of that test entirely.
Meanwhile, there are established methods for parallel implementation of L2 regression model, which can be applied to our approach to efficiently process a large number of noisy images.
Statistical analysis involved the implementation of logistic regression models.
The glmnet implementation of lasso regression [ 45, 46] was used for generalized linear modeling.
A linear modelling tool, an implementation of linear regression modelling, allows analysis of several variables at the same time.
Scikit-learn 29 implementation of logistic regression with l2 regularisation was used to create the risk prediction model.
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