RNAchallenge dataset for the classification of protein-coding and non-coding RNAs
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Dec 4, 2022
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false-negative
Here are 11 public repositories matching this topic...
[IJHCS] UTA7: a dataset of rates (BI-RADS) provided by clinicians resulted from classifying the given medical images for breast cancer diagnosis.
medical-imaging human-computer-interaction user-testing user-centered-design breast-cancer false-positive false-negative birads bi-rads false-positive-rate false-negative-rate
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Apr 13, 2023 - CSS
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.
logistic-regression precision false-positive heart-disease false-negative type-1-and-type-2-errors cap-curve
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Jun 22, 2024 - Jupyter Notebook
Streamlit dashboard for visual-inspection model evaluation, FP/FN analysis, threshold tuning, and report export.
python computer-vision evaluation false-positive false-negative defect-detection visual-inspection streamlit threshold-tuning
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Jun 28, 2026 - Python
feature-detection biological-data-analysis benchmark-framework feature-tracking live-cell-imaging ground-truth graphical-user-interface false-positive false-negative biological-data-visualization time-lapse-dataset software-benchmarking epifluorescence-microscopy benchmarking-metrics fluorescent-speckle-microscopy low-signal-to-noise-ratio software-evaluation subresolution-cluster-of-fluorophores algorithm-validation wide-field-microscopy
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Sep 13, 2026 - MATLAB
A tool reading ground truth data and detected object data provides visualization way to estimate position accuracy.
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Sep 29, 2017 - Java
Slides of a talk on deep learning, false negatives/positives and predator-prey interactions with R.
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Mar 31, 2021 - JavaScript
UC Berkeley LS 22 Spring 2020
education data-science jupyter-notebook uc-berkeley false-positive false-negative statistical-uncertainties sense-sensibility-science
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Jul 2, 2025 - Jupyter Notebook
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Sep 29, 2017 - MATLAB
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…
machine-learning svm scikit-learn ensemble-learning confusion-matrix adaboost decision-trees gradient-boosting statstics false-positive random-forest-classifier false-negative logisticsregression roc-curves recall-precision smoteenn smote-oversampler
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Jul 24, 2020 - Jupyter Notebook
Quarantine, audit and replay rejected AI decisions
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Oct 4, 2026 - TypeScript
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