Results for “metrics-constellation”

Build the funnel metrics that actually get trusted. Stage-by-stage conversion, velocity, win rate, and the single biggest leak, with every definition pinned so nobody relitigates the numbers in the meeting. Built for B2B RevOps teams, customizable to your CRM and your stage model. Trigger on "build my funnel metrics", "what's my conversion by stage", "where's the leak", "what's our win rate", "how fast do deals move", or any funnel diagnostic.

Quantifies the minimum accuracy gap needed between two models for a sampled micro-benchmark to reliably preserve their ranking, using the MDAD metric from Yauney et al. (2025).

Evaluates the robustness and alignment with human judgment of reference-based and reference-free evaluation metrics for machine translation and summarization, particularly under adversarial conditions.

Analyzes stellar structure, galactic dynamics, orbital mechanics, gravitational lensing, cosmic microwave background, and exoplanet detection methods.

This skill should be used when the user asks about "key startup metrics", "SaaS metrics", "CAC and LTV", "unit economics", "burn multiple", "rule of 40", "marketplace metrics", or requests guidance on tracking and optimizing business performance metrics.

Every metric encodes an opinion about which mistake hurts.

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

Control LLM output with regex and grammars, guarantee valid JSON/XML/code generation, enforce structured formats, and build multi-step workflows with Guidance - Microsoft Research's constrained generation framework

Computes the AUROC metric using torchmetrics, handling binary, multiclass, and multilabel tasks with configurable thresholds and averaging.

Use for writing, reviewing, debugging, testing, or diagnosing Python Bayesian models built directly with PyMC, including Model, coords/dims, Data, random variables, potentials, posterior sampling, prior/posterior predictive checks, and InferenceData output. Trigger on model geometry, shape errors, divergences, sampler choice, mutable prediction data, and probabilistic validation. Do not use for Bambi formula models, NumPyro/JAX programs, ArviZ-only analysis of existing draws, deterministic optimization, or general statistics without PyMC code.

Bayesian modeling with PyMC. Build hierarchical models, MCMC (NUTS), variational inference, LOO/WAIC comparison, posterior checks, for probabilistic programming and inference.

Bayesian modeling with PyMC. Build hierarchical models, MCMC (NUTS), variational inference, LOO/WAIC comparison, posterior checks, for probabilistic programming and inference.

Computes the Receiver Operating Characteristic (ROC) metric using torchmetrics, supporting binary, multiclass, and multilabel tasks.

Computes the SQuAD metric using torchmetrics, given predictions and ground truth. Use when evaluating question-answering outputs with exact match and F1 scores.

Measures inference latency of binarized, 8-bit, and 32-bit convolutional layers on edge devices to evaluate the efficiency and speedup of the Larq Compute Engine framework compared to standard implementations.