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Testing & QA
Unit tests, integration tests, E2E, QA, validation, and test automation
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- Multi Seed Fold AveragingTrains multiple models per CV fold with different random seeds for augmentation, then averages their predictions to reduce variance from stochastic data generation.Votes: 0GitHub stars: 61
- Multi Input Embedding NnKeras multi-input model with separate embedding layers for categoricals, GRU for text sequences, and dense layers for numerics, all concatenated into a shared regression trunkVotes: 0GitHub stars: 61
- Anonymized Target Reverse EngineeringProbe an anonymized regression target by testing whether simple invertible transforms (2**y, exp(y), log(y), affine rescale) produce a distribution with recognizable structure — round numbers, integer histograms, or a familiar finance/retail range — and use the recovered semantics to motivate features and loss choices the host's bland description would never suggestVotes: 0GitHub stars: 61
- Adversarial ValidationTrains a classifier to distinguish train from test data, detecting distribution shift and identifying leaked features.Votes: 0GitHub stars: 61
- Validation Finetune ContinuationAfter training on the primary split, continues fine-tuning on the validation set to adapt the model to the target distribution before inference.Votes: 0GitHub stars: 61
- Sentence Alignment AugmentationSplit multi-sentence parallel pairs into aligned sentence pairs to expand training data for seq2seq modelsVotes: 0GitHub stars: 61
- Pearson Correlation MetricUse Pearson correlation coefficient as evaluation metric for semantic similarity regression tasks, selecting best checkpoint by correlation rather than lossVotes: 0GitHub stars: 61
- Pairwise Ranking ValidationEvaluates ranking models by computing the fraction of preference pairs where the model correctly scores the preferred item higher.Votes: 0GitHub stars: 61
- Domain Wordpiece TfidfTrains a WordPiece tokenizer on in-domain text, then feeds its subword token IDs into TF-IDF vectorization for domain-adapted sparse features.Votes: 0GitHub stars: 61
- Cls Null Score Span RerankingReranks candidate answer spans by subtracting the CLS token's start+end logit sum as a null-answer baseline score.Votes: 0GitHub stars: 61
- Category Weighted Score FusionConverts multi-label binary flags into a continuous regression target by applying hand-tuned per-category multipliers, then averaging across categories.Votes: 0GitHub stars: 61
- Weighted Embedding BlendingEnsemble predictions from heterogeneous vision-language models by blending their output embeddings with fixed scalar weights in embedding spaceVotes: 0GitHub stars: 61
- Soft Threshold Count AggregationAggregate patch-level count-regression predictions into image-level totals by multiplying each prediction by a boolean mask (x * (x > tau)) instead of rounding, retaining fractional evidence above threshold while killing background noiseVotes: 0GitHub stars: 61
- Siamese Pairwise Comparison HeadSiamese network head that compares two embeddings via element-wise multiply, add, abs-diff, and squared-diff features for verification tasksVotes: 0GitHub stars: 61
- Quantile Threshold Prevalence MatchingSets the binary classification threshold as a prediction quantile matching the expected positive prevalence rate, avoiding manual threshold tuning.Votes: 0GitHub stars: 61
- Pfbeta Threshold OptimizationGrid-searches the optimal classification threshold to maximize probabilistic F-beta score on validation predictions.Votes: 0GitHub stars: 61
- Per Label Platt Isotonic CalibrationFit a per-label probability calibrator on out-of-fold scores using Platt scaling (logistic regression on raw scores) and fall back to isotonic regression for labels where the logistic doesn't converge — pickle the dict of fitted calibrators and apply at inference for a small but free leaderboard lift on multi-label classificationVotes: 0GitHub stars: 61
- Patient Level Stratified KfoldStratifies CV folds at the patient level rather than image level, preventing data leakage when multiple images exist per patient.Votes: 0GitHub stars: 61
- Ordinal Multilabel EncodingEncodes ordinal classes as cumulative binary labels (class N activates labels 0..N), enabling sigmoid + BCE training for ordinal regression.Votes: 0GitHub stars: 61
- Optimized Ordinal ThresholdsUses Nelder-Mead optimization to find per-class decision thresholds that maximize Quadratic Weighted Kappa for regression-to-ordinal conversion.Votes: 0GitHub stars: 61
- Lateralized Label Flip Tta DisableDisable horizontal-flip augmentation (both train-time and TTA) when label columns encode left/right anatomy — flipping silently corrupts the targets because "Left ICA" must map to "Right ICA" after a flip, not stay as "Left ICA"Votes: 0GitHub stars: 61
- Iou Threshold SweepGrid search binarization thresholds on validation predictions to find the cutoff that maximizes mean IoUVotes: 0GitHub stars: 61
- Cross View Count Consistency FilterDrop false-positive detections by requiring matched per-frame box counts across paired synchronized camera views (Endzone vs Sideline) — if both views see the same scene at the same instant, true events should appear in bothVotes: 0GitHub stars: 61
- Coverage Stratified SplitStratify train/validation split by binned mask coverage percentage to ensure balanced foreground representation in segmentation tasksVotes: 0GitHub stars: 61