LIME (Local Interpretable Model-Agnostic Explanations) is a technique for explaining individual predictions of any black-box machine learning model. Introduced by Ribeiro et al. in 2016, LIME works by perturbing the input around a specific data point, observing how the model's predictions change, and fitting a simple interpretable model (typically linear regression) to those local perturbations. The result is a set of feature weights that approximate why the black-box model made its decision for that particular instance. LIME is widely used in production systems where explainability is required but the underlying model — a neural network, ensemble, or third-party API — cannot be introspected directly.
Analogy🏏Cricket
🏏 Think of it like cricket: Evidently AI is the IPL's official analytics platform — rather than each franchise building their own stats system, they use a shared platform that automatically computes every standardized metric: batting averages, economy rates, strike rates, net run rates. When Virat Kohli's performance drifts from his baseline, the platform highlights it automatically with charts. Evidently does the same for ML models: instead of each team coding their own drift detectors, they use Evidently's pre-built metrics and get standardized, comparable reports automatically. The standardization is the strategic point, not a convenience: because every franchise reads the same metric definitions, a drift score of 0.3 means the same thing in every dashboard, reports can be compared across teams and seasons, and a new analyst is productive on day one. Hand-rolled monitoring scripts fail exactly here — every team's 'drift check' quietly means something different, and nobody can audit whose alarm was right.