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资源简介：
Table S1: QDA Training Sets and Validation Sets; Table S2: LDA Misclassification.
应用场景：
创建时间：
2023-06-01
相关数据集
Additional file 5 of Texture-based image feature analysis for the classification of oral squamous cell carcinoma using machine learning approach
Additional file 5. Table of results for High power set with 5-fold cross validation with patient split with a new, separate test set of patients.
Figshare2025-12-27 更新60
Additional file 10 of Texture-based image feature analysis for the classification of oral squamous cell carcinoma using machine learning approach
Additional file 10. Table of results for clinical data only with cross validation with patient split after ablation of the age feature.
Figshare2025-12-27 更新50
General discriminant analysis (GDA) models for discriminating between TB disease and no TB in all study participants.
Participants were not stratified according to Quantiferon results or HIV status prior to analysis. In each case, effect df = 1, error df = 71. P- values for all the models were
Figshare2015-12-02 更新10
Additional file 8 of Texture-based image feature analysis for the classification of oral squamous cell carcinoma using machine learning approach
Additional file 8. Tables of results for clinical data only with cross validation with only one image with only one clinical data for every patient.
Figshare2025-12-27 更新50
Classification table of correct estimates based on FISH variables.
Statistical parameters of the discriminant model: Wilks's lambda coefficient – 0.045. Significance of the model: F (20,16) = 2.95; p*apriori probability of correct classification.
Figshare2015-12-02 更新40
