SHAP vs Lime
Side-by-side — pricing, strengths, tradeoffs, and fit.
SHAP
Game theoretic approach to explain machine learning model outputs.
Lime
Explains black box classifiers with two or more classes.
At a glance
Open SourceFree Tier
● Established·From $0
Open SourceFree Tier
◌ Declining·From $0
Overview
One-liner
One-liner
Game theoretic approach to explain machine learning model outputs.
One-liner
Explains black box classifiers with two or more classes.
Maturity
Maturity
EstablishedMaturity
DecliningOpen source
Open source
✓ YesOpen source
✓ YesPricing
Free tier
Free tier
✓ AvailableOpen source — free to use
Free tier
✓ AvailableOpen source — free to use
Starts at
Starts at
$0
Starts at
$0
Model
Model
Flat rate
Model
Flat rate
Enterprise
Enterprise
✕ NoneEnterprise
✕ NoneBest for
✓ Best for
Teams needing detailed explanations of individual predictions from complex models
Projects where model transparency and explainability are critical for regulatory compliance
Developers looking to improve the interpretability of their machine learning systems
✓ Best for
Teams needing to understand complex machine learning models without access to the model's internal workings.
Projects where transparency and explainability are critical for regulatory compliance.
Not ideal for
✕ Not for
Applications requiring real-time explanation generation with high latency constraints
Scenarios where the computational overhead of SHAP is prohibitive due to large datasets or complex models
✕ Not for
Scenarios requiring real-time explanations of predictions due to computational overhead.
Use cases that do not require understanding individual prediction rationales, focusing solely on overall accuracy.
Strengths
↑ Strengths
Game theoretic approach to explain model outputs
Unified measure of feature importance
Supports a wide range of machine learning models
Provides both global and local explanations
↑ Strengths
Explains black box models
Works with any classifier
Provides local explanations for individual predictions
Weaknesses
↓ Weaknesses
Limited performance with large datasets
Complex setup for integrating with existing ML pipelines
↓ Weaknesses
Limited support for real-time model monitoring and updates
Performance overhead for large datasets or complex models