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Finance and trading
credit-scoring
credit scoring
FICO
System
Techniques · 15
Primitives · 46
credit-scoring
Distribution drift detection
machine-learning · medium: PSI, KS and other tests for feature and score drift between training and production
Feature store
machine-learning · medium: One definition per feature feeds a history store for training and a latest-value store for serving, so training and inference see the same values as of the same moment.
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Gradient-boosted decision trees (GB…
machine-learning · medium: Add small regression trees one at a time, each fitted to the loss gradient; histogram splits over 256 bins make it the strongest tabular default.
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Shapley values treeshap
machine-learning · medium: TreeSHAP and Shapley attributions for per-prediction explanations and reason codes
Bitemporal data
storage · medium: Valid time plus transaction time on every record for as-of queries, back-dating and corrections
Entity resolution
storage · medium: Decide which records describe the same real-world thing: block to cut the pair space, score pairs with a probabilistic model, cluster the matches transitively.
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Windowed stream aggregation
distributed · medium: Group events by event-time window per key, keep one partial aggregate per slice, and emit a window when the watermark passes its end.
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Linear and logistic regression
machine-learning · low: Fit a weighted sum of features to a number (least squares) or a probability (logistic); the baseline every model must beat.
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Fuzzy string matching
search-sort · low: Edit distance, Jaro-Winkler and trigram indexes for approximate name and address matching
Calibration
machine-learning · low: Make predicted probabilities match observed frequencies: measure with ECE, fix post hoc with temperature, Platt or isotonic scaling on held-out data.
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Evaluation metrics
machine-learning · low: Precision, recall, ROC AUC, average precision, log loss, NDCG and pass@k computed correctly, plus paired confidence intervals for comparing models.
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Fairness metrics
machine-learning · low: Disparate impact, equalized odds and calibration-by-group checks for models
Weight of evidence binning
machine-learning · low: Monotone binning and WoE/IV transformation for scorecard models
Time-series cross-validation (rolli…
statistics · low: Estimate forecast error by repeatedly training on the past and testing on the next h steps, moving the origin forward; never trains on the future.
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Quantile sketches
data-structures · low: Mergeable quantile sketches: t-digest, DDSketch, HDR histograms
Columnar file format
storage · low: Row groups, column chunks, pages, encodings and footer stats (Parquet/ORC style) (listed in Moved to the wiki)
Loss functions
machine-learning · low: The scalar a model minimizes: cross-entropy for classes and tokens, MSE or Huber for values, each with a stable form and simple gradient.
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Regularization
machine-learning · low: Techniques that trade training fit for generalization: weight decay, dropout, early stopping, label smoothing and data mixing, with the exact update rules and defaults.
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Monte Carlo integration
numerics · low: Estimate an expectation by averaging random samples; error falls as σ/√N in any dimension, and variance reduction (antithetic, control variates, importance sampling) shrinks σ.
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Interval tree
data-structures · low: Index of ranges that answers "which intervals overlap this point or range?" in O(log n + k); static sorted-array form is the compact, cache-friendly choice.
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Time partitioning
storage · low: Cut a table into fixed-interval time blocks so queries read only overlapping blocks and retention drops a whole block as one metadata change.
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Union-find
graphs · low: Disjoint-set forest with union by size and path compression; merges sets and answers same-set queries in effectively constant time.
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Embeddings
machine-learning · low: A learned table mapping discrete ids to dense vectors: gather forward, sparse scatter-add backward, row-wise optimizers to keep huge tables cheap.
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+ 8 more techniques
+ 30 more primitives
System
Techniques · 15
Primitives · 46
credit-scoring
Distribution drift detection
machine-learning · medium: PSI, KS and other tests for feature and score drift between training and production
Feature store
machine-learning · medium: One definition per feature feeds a history store for training and a latest-value store for serving, so training and inference see the same values as of the same moment.
›
Gradient-boosted decision trees (GB…
machine-learning · medium: Add small regression trees one at a time, each fitted to the loss gradient; histogram splits over 256 bins make it the strongest tabular default.
›
Shapley values treeshap
machine-learning · medium: TreeSHAP and Shapley attributions for per-prediction explanations and reason codes
Bitemporal data
storage · medium: Valid time plus transaction time on every record for as-of queries, back-dating and corrections
Entity resolution
storage · medium: Decide which records describe the same real-world thing: block to cut the pair space, score pairs with a probabilistic model, cluster the matches transitively.
›
Windowed stream aggregation
distributed · medium: Group events by event-time window per key, keep one partial aggregate per slice, and emit a window when the watermark passes its end.
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Record linkage
search-sort · medium: Probabilistic record matching (Fellegi-Sunter) with blocking for patient or entity indexes
Hypothesis testing
statistics · medium: Pick a test statistic, compare it to its distribution under "no effect", read off a p-value, and control error rates across one test or many.
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Inverted index
storage · medium: Map each term to a sorted, compressed list of the documents containing it; the core of full-text search and other fast set queries.
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LSM-tree
storage · medium: Buffer writes in memory, flush them as immutable sorted files and merge in the background; turns random writes into sequential I/O.
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MVCC
storage · medium: Keep timestamped versions of each row so readers see a consistent snapshot without blocking writers; the default concurrency control in modern databases.
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Point in time join
storage · medium: As-of join that picks the latest feature value at or before each event timestamp
Watermarks
distributed · medium: Event-time progress markers that bound lateness and trigger window results
Near-duplicate detection with MinHa…
machine-learning · medium: Estimate set Jaccard similarity from k min-hashes and find similar pairs with banded LSH buckets; the standard dedup for web crawls, training corpora and sequence collections.
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Linear and logistic regression
machine-learning · low: Fit a weighted sum of features to a number (least squares) or a probability (logistic); the baseline every model must beat.
›
Fuzzy string matching
search-sort · low: Edit distance, Jaro-Winkler and trigram indexes for approximate name and address matching
Calibration
machine-learning · low: Make predicted probabilities match observed frequencies: measure with ECE, fix post hoc with temperature, Platt or isotonic scaling on held-out data.
›
Evaluation metrics
machine-learning · low: Precision, recall, ROC AUC, average precision, log loss, NDCG and pass@k computed correctly, plus paired confidence intervals for comparing models.
›
Fairness metrics
machine-learning · low: Disparate impact, equalized odds and calibration-by-group checks for models
Weight of evidence binning
machine-learning · low: Monotone binning and WoE/IV transformation for scorecard models
Time-series cross-validation (rolli…
statistics · low: Estimate forecast error by repeatedly training on the past and testing on the next h steps, moving the origin forward; never trains on the future.
›
Quantile sketches
data-structures · low: Mergeable quantile sketches: t-digest, DDSketch, HDR histograms
Columnar file format
storage · low: Row groups, column chunks, pages, encodings and footer stats (Parquet/ORC style) (listed in Moved to the wiki)
Loss functions
machine-learning · low: The scalar a model minimizes: cross-entropy for classes and tokens, MSE or Huber for values, each with a stable form and simple gradient.
›
Regularization
machine-learning · low: Techniques that trade training fit for generalization: weight decay, dropout, early stopping, label smoothing and data mixing, with the exact update rules and defaults.
›
Monte Carlo integration
numerics · low: Estimate an expectation by averaging random samples; error falls as σ/√N in any dimension, and variance reduction (antithetic, control variates, importance sampling) shrinks σ.
›
Interval tree
data-structures · low: Index of ranges that answers "which intervals overlap this point or range?" in O(log n + k); static sorted-array form is the compact, cache-friendly choice.
›
Time partitioning
storage · low: Cut a table into fixed-interval time blocks so queries read only overlapping blocks and retention drops a whole block as one metadata change.
›
Union-find
graphs · low: Disjoint-set forest with union by size and path compression; merges sets and answers same-set queries in effectively constant time.
›
Embeddings
machine-learning · low: A learned table mapping discrete ids to dense vectors: gather forward, sparse scatter-add backward, row-wise optimizers to keep huge tables cheap.
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Hash aggregation
storage · low: GROUP BY via hash tables with partitioning and spill to disk
Hierarchical timing wheel
systems · low: Timers bucketed by expiry into wheels of slots, coarser wheels for far deadlines: O(1) insert and cancel for millions of timeouts.
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Bootstrap confidence intervals
statistics · low: Resampling-based confidence intervals for arbitrary statistics
Wilson score interval
statistics · low: Wilson lower bound for ranking by up/down votes and proportions with few samples
Bit packing and PFOR
compression · low: Store blocks of integers with exactly b bits each and patch rare outliers separately (PFOR); the fastest integer codec family, billions of values per second.
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Delta encoding
compression · low: Store differences between consecutive values instead of the values, turning sorted IDs, timestamps and counters into small numbers that pack into few bits.
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Varint (LEB128)
compression · low: Variable-length integers in 7-bit groups with a continuation bit; small numbers take 1 byte, the wire format of Protobuf, WebAssembly and DWARF.
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BM25 ranking
search-sort · low: The standard lexical relevance score: IDF-weighted term frequency with saturation (k1) and length normalization (b); default in Lucene, Elasticsearch and SQLite FTS5.
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Sorted-list intersection
search-sort · low: Intersect sorted id lists by merging, galloping from the short list into the long one, and skipping whole compressed blocks, shortest list first.
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Sort-merge join
storage · low: Sort both inputs on the key, then advance two cursors in lockstep and emit matching runs; cheap on sorted inputs and good for range joins.
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Window functions
storage · low: ROW_NUMBER, rank, running and frame aggregates via sort and segment trees
d-ary heap
data-structures · low: Array heap with d children per node (d = 4 typical): half the binary heap's depth, siblings in one cache line, faster priority queues.
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Consumer offsets
distributed · low: Each consumer group stores one integer per partition, the next position to read, so a restarted consumer resumes where it left off with at-least-once delivery.
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Fast hash functions
data-structures · low: Non-cryptographic hashes built from 64×64→128-bit multiplies and xor-shifts; pick by key size, trust level and whether the hash is persisted.
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Rolling hash
data-structures · low: Rabin-Karp polynomial rolling hashes and winnowing for substring fingerprints and document matching
Swiss table
data-structures · low: Open-addressing hash table that filters 8-16 slots at once with 1-byte hash tags and SIMD, giving fast lookups at 87.5 % load.
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Fencing tokens
distributed · low: A number that grows with every lock grant and is checked by the protected resource, so a paused ex-holder's late write is rejected.
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Message sequence numbers
distributed · low: A per-stream counter on every message lets the receiver detect loss, duplicates and reordering, ack cheaply and resume after reconnect with "send from N".
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Segmented log
distributed · low: Split an append-only log into files named by their first offset, write only the newest, and find any record by binary search plus a sparse index.
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SIMD programming
hardware-os · low: Process 4-16 lanes per instruction with AVX2, AVX-512 or NEON via Rust std::arch, with runtime dispatch and a portable fallback.
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Text normalization
machine-learning · low: Rewrite messy text into one canonical form for matchers and models, or into spoken words for speech output (and back), keeping a map to the original offsets.
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AdamW
ml-training · low: Adam's per-parameter adaptive steps with weight decay applied directly to the weights; the default optimizer for transformers and LLM pretraining.
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Rejection sampling
ml-training · low: Best-of-n and filtered sampling: generate many candidates, keep those passing a verifier or reward threshold
Reverse-mode automatic differentiat…
ml-training · low: Record the forward computation on a tape, then sweep it backwards once to get the gradient of a scalar loss for all inputs.
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L-BFGS
numerics · low: Quasi-Newton minimizer that approximates the inverse Hessian from the last m gradient differences via the two-loop recursion; O(m·n) memory and work per step.
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LU and Cholesky factorization
numerics · low: Factor A = PLU (any square matrix, partial pivoting) or A = LLᵀ (SPD) once in O(n³), then solve each right-hand side in O(n²).
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PRNGs (PCG, xoshiro)
numerics · low: Small, fast, statistically strong non-cryptographic generators: PCG32 and xoshiro256**, with seeding, parallel streams and unbiased ranges.
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Top-k selection
search-sort · low: Keep the k best of n items without sorting all n - bounded min-heap for streams, quickselect for arrays, threshold pruning to skip most items.
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Unicode text tokenization for search
search-sort · low: Turn multilingual text into index terms: segment words (UAX 29 style, CJK bigrams), normalize, case-fold and accent-fold, identically at index and query time.
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Slab allocator
systems · low: Pre-carve pages into equal-size slots per size class with an in-place free list: O(1) alloc and free, no external fragmentation, cache-warm reuse.
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