Random Forest also showed results comparable to XGBoost, with high accuracy and precision. In contrast, Logistic Regression displayed limitations in handling complex data, with lower performance across all evaluation metrics. This study demonstrates that decision-tree-based algorithms li...
In this study, Random Forest regression algorithm was used to determine the effect of climate change on the distribution areas of Pistacia terebinthus L. in T眉rkiye. For the model, asset data of Terebinth and current and future bioclimatic variables were used. In ...
Random Forest, Artificial Neural Network, Logistic Regression, Support Vector Machines, and Naive Bayes. The author's team then tested his 373 Alzheimer's disease patient data from Kaggle Open Datasets and showed that the Logistic Regression algorithm model can achieve better with 85,71% accuracy ...
Additionally, NB algorithms were compared with advanced machine learning algorithms such as Logistic Regression, Random Forest, Linear Support Vector Classifier, and Multi-Layer Perceptron. The Multi-Layer Perceptron stood out with an accuracy rate of 98.31%, while the other algorithms...
The model with the highest accuracy value is the model with the Support Vector Regression (SVR) algorithm with a linear kernel and an epsilon (蔚) value of 0.1, with an accuracy value of 0.938 on test data and 0.958 on new data.Cahyadi, Hasani Abdulazizi...
In this study, 6 classification methods such as Naive Bayes (NB), Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF) and Artificial Neural Network (ANN) were used by using libraries in Python programming language. Within the...
LOGISTIC regression analysisIn this study, it is aimed to classify the factors affecting calf diseases with Artificial Neural Networks (YSA), Random Forest Algorithm (RO) and Logistic Regression Analysis (LR), to reveal the usability of these methods and to compare their ...
According to the analysis results, random forest and extreme gradient increment regression algorithms were the algorithms with the most successful results. When the mean square error (MSE) values are examined, the best results are observed in the data set consisting of ...
In this study, heart disease prediction was performed using Logistic Regression, Decision Trees, Random Forest, K Nearest Neighbors, Naive Bayes, Gradient Boosting, XGBoost, and Bagging machine learning algorithms. Four separate datasets were created using data balancing methods suc...
The algorithms tested include Logistic Regression, K-Nearest Neighbors (KNN), Decision Tree, Random Forest, GaussianNB, and LightGBM. The results show that the Random Forest algorithm provided the best performance with an accuracy of 95 percent, while the KNN algorithm showed the...