Learn some tips and techniques to improve the accuracy of your classification model in data mining, such as choosing the right algorithm, preprocessing your data, tuning your parameters ...

To increase the accuracy of image classification using CNNs, hyperparameter tuning is crucial. This involves optimizing parameters like learning rate, batch size, and dropout rate to enhance model ...

When choosing the right AI model for your enterprise, don’t just ask how well it scores. Ask how well it works for your ...

Classification of reliability values for Bus Rapid Transit (BRT) is very important to ensure services run smoothly and meet passenger expectations. Comparison of scaler variants is used in the KNN and ...

In addition, RcdNet’s F1-score is 0.9290, indicating that its ability to balance precision and recall makes the overall classification effect of the model more robust. In terms of accuracy, RcdNet ...

The substantial increase in mental health disorders globally necessitates scalable, accurate tools for detecting and classifying these conditions in digital environments. This study addresses the ...

If accuracy and F1-score are both above 0.80, the model is typically considered production-ready in many business scenarios. If recall is low , the model may be missing important cases—critical ...

Logistic regression is the most cost-effective model for medial vascular calcification classification, with a mean ICER of $278 using five low-cost features. Despite similar diagnostic accuracy ...