Results for “epsilon”

Query EAS Update health metrics from the CLI: crash rates, install/launch counts, unique users, payload size, and embedded vs OTA user splits per channel.

Build clinical deep-learning pipelines with PyHealth: load EHR, signal, and imaging datasets, define prediction tasks, instantiate models, train with the PyHealth Trainer, and compute clinical metrics.

Returns structured prediction-market data for Polymarket, Kalshi, Limitless, Opinion, Predict.Fun, dFlow, and UMA oracle via a local API, covering markets, cross-venue search, leaderboards, smart money, wallet analytics, identity clustering, and resolution status.

Analyzes Stockbee-style Day 1 Episodic Pivot candidates by scoring catalyst quality alongside price/volume confirmation, gap/range expansion, volume shock, neglect/revaluation context, liquidity, and risk to the EP-day low.

Computes the Anderson-Darling test statistic and p-value using scipy.stats.anderson for evaluating predictions against ground truth.

Extract, parse, and analyze Windows Event Logs (EVTX) using Chainsaw, Hayabusa, and EvtxECmd to detect lateral movement, persistence, and privilege escalation.

Perform time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search using a scikit-learn compatible Python toolkit.

Builds statistical plots with the seaborn Python library and pandas DataFrame integration, on attractive matplotlib-based defaults. Use for quick exploration of distributions, relationships, and categorical comparisons — box plots, violin plots, swarm/strip plots, KDE/histograms, pair plots, joint plots, regression plots, correlation heatmaps, and faceted small multiples (relplot/displot/catplot/lmplot). For interactive/hover/zoom charts defer to alterlab-plotly; for exact journal/manuscript styling (column widths, point fonts, CMYK, vector export) defer to alterlab-scientific-viz; for low-level custom matplotlib figures defer to alterlab-matplotlib (seaborn integrates with it for fine-tuning). Part of the AlterLab Academic Skills suite.

Generates OMG-compliant BPMN 2.0 XML and SVG diagrams from natural language process descriptions, with validation, automatic layout, and optional process optimization advisories.

Grades and classifies evaluation batch results, applying exclusions, diagnosing failure modes, computing pass rates, and generating summary tables for papers.