Recently, various ensemble learning methods with different base classifiers have been proposed for credit scoring problems. However, for various reasons, there has been little research using logistic regression as the base classifier. In this paper, given large unbalanced data, we consider the plausibil...
We compare the classification performance of FMM-LASSO with that of support vector machine (SVM), Random Forest, K-nearest Neighbor (KNN), Nave Bayes and Logistic Regression classifiers, with and without LASSO. The results prove that FMM-LASSO performs better as compared to other approaches.Singh...
This is in contrast to making categorical predictions such as “will someone buy/not buy” or “will/will not fail,” where classification tools such as decision trees or logistic regression models are used. In order to ensure regression models are not arbitrarily deployed, several checks must ...
For example, using a small scale for \(\sqrt{{\omega }_{1}}\) such as e−5, the R function bayesglm in R package ARM (which implements penalized logistic regression with Cauchy priors) will converge to a mode where almost all coefficients are shrunken to very small values, even ...
In this sense, Lasso logistic regression is preferable to random forest as far as transportability and sustainability are concerned. Note that model interpretation is also particularly easy with sparse penalized regression methods. Finally, coming back to prediction accuracy, we note that medical ...
With this result, we see that even if the model is not linear, and even if the response is not continuous, we could still use vanilla Lasso to train classifiers. Simulations confirm that vanilla Lasso could be used to get a good estimation when data are generated from a logistic regression...
assert_less(error,0.01)# similar test, with the classifiersforalphainnp.linspace(1e-2,1-1e-2,20): clf1 = linear_model.LassoLars(alpha=alpha, normalize=False).fit(X, y) clf2 = linear_model.Lasso(alpha=alpha, tol=1e-8, normalize=False).fit(X, y) ...
We further show that, using different types of classifiers such as logistic regression, naive Bayes, support vector machines, decision trees and Random Forest, the classification performance of Tree-Lasso is comparable to Lasso and better than other methods. Our result has implications in identifying...
Based on the four real-world credit risk datasets, the LASSO-MCOC with linear and RBF kernels are tested and compared with the SMCOC proposed by Zhang et al. (2019) and six basic classification methods including logistic regression, multilayer perceptron, support vector machines, Nave Bayes, k...
Keywords: Cox regression, Lasso, Multi-omics data, Penalized regression, Prediction model, Priority-lasso Background Many cancers are heterogeneous diseases regarding biol- ogy, treatment response and outcome. For example, in the context of acute myeloid leukemia (AML), a vari- ety of classifiers ...