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Classification on Breast Cancer

98.25Accuracy

SVM

Updated 25d ago

Evaluation Results

Method	Links
SVM 2024.07		98.25
Logistic Regression 2024.07		98.07
Neural Network 2024.07		97.82
XGB 2025.09		97.73
XGB 2025.09		97.73
Logistic Regression 2024.07		97.72
SVM 2024.07		97.72
AdaBoost 2024.07		97.72
AdaBoost 2024.07		97.55
XGBoost 2024.07		97.54
XGB 2025.09		97.54
XGB 2025.09		97.53
CSVM 2025.04		97.4
BH-t-SNE 2026.05		97.3
XGBoost 2024.07		97.19
XGB 2025.09		97.18
XGB 2025.09		97.16
openTSNE 2026.05		97.1
Neural Network 2024.07		97.01
GB 2025.09		96.98
RF 2025.09		96.98
XGB 2025.09		96.98
Random Forest 2024.07		96.84
Gradient Boost 2024.07		96.84
XGB 2025.09		96.81
RF 2025.09		96.78
RF 2025.09		96.78
XGB 2025.09		96.78
UMAP 2026.05		96.7
RF 2025.09		96.61
Bagging 2024.07		96.49
RF 2025.09		96.42
FastUMAP 2026.05		96.4
GB 2025.09		96.24
GB 2025.09		96.23
GB 2025.09		96.23
RF 2025.09		96.06
GB 2025.09		96.05
GB 2025.09		96.03
GB 2025.09		96.03
GB 2025.09		96.03
SUDE 2026.05		96
Random Forest 2024.07		95.96
GB 2025.09		95.87
RF 2025.09		95.85
RF 2025.09		95.84
TSVM 2025.04		95.6
RF 2025.09		95.47
LogReg 2025.09		95.26
LogReg 2025.09		95.08
Bagging 2024.07		94.9
LogReg 2025.09		94.89
LogReg 2025.09		94.88
LogReg 2025.09		94.87
Gradient Boost 2024.07		94.74
LogReg 2025.09		94.33
LogReg 2025.09		94.31
Decision Tree 2024.07		94.26
LogReg 2025.09		93.76
LogReg 2025.09		93.74
Decision Tree 2024.07		93.32
Comparable models 2026.06		86