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Ultimately, we employed decision trees, logistic regression, and random forests to reach our objective. Of these, random forest yielded the highest accuracy of 96%, making them useful for obtaining ...
Over the past decades, classification models have proven to be essential machine learning tools given their potential and applicability in various domains. In these years, the north of the majority of ...
The application of RF to ToF-SIMS imaging facilitates the classification of complex chemical compositions and the identification of features contributing to these classifications. This tutorial aims ...
Random Forest is an ensemble learning method that constructs multiple decision trees during training and outputs the mode of the classes (classification) or mean prediction (regression) of the ...
This project implements both a custom random forest classifier for binary classification tasks and a custom random forest regressor for regression tasks. It consists of the following main Python files ...
Accelerating a Random Forest Classifier: Multi-Core, GP-GPU, or FPGA? Abstract: Random forest classification is a well known machine learning technique that generates classifiers in the form of an ...
Using the Random Forest (RF) classifier with n_estimators = 400, criterion = gini, min_samples_split = 18, and changing the threshold probabilities, a threshold-probability graph was obtained.