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For the data set and parameters considered logistic regression has shown better performance.
We provide logistic regression equations that can be applied in similar clinical settings to assist in determining performance validity.
They contend that logistic regression can be a powerful statistical procedure when used appropriately.

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Can the contribution of features in logistic regression machine learning be evaluated by ANOVA analysis?5 answersYes, the contribution of features in logistic regression machine learning can be evaluated using ANOVA analysis. ANOVA (Analysis of Variance) is a statistical method that can be applied to assess the significance of sample regression lines, providing insights into the overall nature of regression analysis. Additionally, ANOVA Discriminant Analysis (ADA) has been utilized to statistically prove the effectiveness of features selected through Decision Tree Induction (DTI) for defect predictions, showing that these selected features have higher discriminating power and can improve classification accuracy in software defect predictions. Moreover, a novel algorithm combining ANOVA ranking of principal components, bootstrap, and classification models has been proposed to automatically select the number of principal components in PCA, enhancing the accuracy of logistic regression models by selecting the best combination of principal components.
What is logistic regression in machine learning?5 answersLogistic regression is a method of statistics closely related to machine learning. It is a popular binary classification technique used in various industries, including the financial service industry. Logistic regression is a supervised machine learning algorithm that is commonly used for classification tasks. It is used to estimate the probability of an event occurring based on a set of input variables. The algorithm works by fitting a logistic function to the data, which allows for the prediction of the probability of a binary outcome. Logistic regression is often used in applications such as credit scorecards, fraud detection, and recommender systems. It has been shown to provide better performance than traditional classification algorithms in certain scenarios.
How to measure logistic performance?5 answersLogistic performance can be measured using various approaches. One proposed procedure involves conducting semi-structured interviews with employees to collect data and identify key processes that contribute to logistical performance. Another method involves using the Performance Prism framework, supported by the Analytical Hierarchy Process, to evaluate performance aspects such as satisfaction, participation, tricks, procedures, and competence of stakeholders. In the context of sea transportation, a Logistic Performance Index (LPI) can be used to assess challenges and opportunities, with key indicators including quality, delivery cost, transportation cost, and information. Additionally, the Bijective soft set approach can be employed to objectively measure performance in the logistics service supply chain, considering quantitative and qualitative indices. Finally, logistic supply chain performance (LSCP) can be measured using a validated measurement scale, with a positive relationship found between LSCP and organizational performance.
How to analyze a logistic regression?4 answersLogistic regression is a statistical technique used to analyze the relationship between independent variables and the probability of occurrence of an event. The effects of the variables are presented through odds, which are the ratio of the probability of an event occurring over the probability of the same event not occurring. Logistic regression is similar to multiple linear regression, but with a binomial response variable. It allows for the examination of the impact of each variable on the odds ratio of the observed event of interest. The goal is to create an equation that estimates the probability of the event based on the independent variables. Hypothesis testing of the regression coefficients can be done using the Wald's test and likelihood ratio test. Logistic regression can be used to analyze the correlation between categorical response variables and predictor variables, and can handle non-linear relationships through techniques like polynomial regression.
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