[IJHCS] UTA7: a dataset of rates (BI-RADS) provided by clinicians resulted from classifying the given medical images for breast cancer diagnosis.
RNAchallenge dataset for the classification of protein-coding and non-coding RNAs
With a precision of 86% and model's CAP curve showing an accuracy of 100%! This means it is capable of correctly predicting 100% of patients with a heart disease after processing 50% of the data. The model's performance is "Too Good to be True"! However, with Train accuracy = 86% and Test accuracy = 82%, there is no visible sign of overfitting.
A tool reading ground truth data and detected object data provides visualization way to estimate position accuracy.
Machine learning for credit card default. Precision-recalls are calculated due to imbalanced data. Confusion matrices and test statistics are compared with each other based on Logit over and under-sampling methods, decision tree, SVM, ensemble learning using Random Forest, Ada Boost and Gradient Boosting. Easy Ensemble AdaBoost classifier appear…
Slides of a talk on deep learning, false negatives/positives and predator-prey interactions with R.
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