Choosing Between LIME, SHAP, Permutation Importance, and Partial Dependence Plots: A Practical Comparison

For most enterprise machine learning teams, the right explainability method comes down to one question first: do you need to explain a single prediction, or the model's overall behavior? SHAP is the only one of the four widely used techniques that answers both questions on a common foundation—but that unification carries a computational price in its general-purpose form. LIME is built for individual predictions, while Permutation Importance and Partial Dependence Plots are global tools that rank and visualize how features shape predictions across the whole model. All four are post-hoc, model-agnostic methods, meaning they work on trained models without requiring access to their internal structure. Here is how the trade-offs actually break down.

Local vs. Global Interpretability: Understanding the Difference

The distinction between local and global explanation is the single most important selection criterion, and it is worth stating precisely. A clinical comparison of six XAI techniques defines the goal of local interpretation as explaining a single prediction by focusing on a particular instance and comprehending how the model reached that prediction. Global interpretation, by contrast, refers to understanding the distribution of the prediction output based on the input features—how the model behaves in general.

In practice, these map onto different business questions. When a loan applicant is denied and a case officer or regulator asks why this decision was made, that is a local question. When a risk team asks which features drive house prices across the entire portfolio, that is a global question. The four methods in this comparison split cleanly along this line: LIME is local, Permutation Importance and Partial Dependence Plots are global, and SHAP spans both. Choosing a method that answers the wrong question is the most common and most costly selection mistake, because no amount of tuning converts a local tool into a global one.

LIME: Explaining Individual Predictions

LIME—Local Interpretable Model-Agnostic Explanations—was ranked second in popularity among XAI methods in a systematic review of black-box model explanation research, behind only SHAP. As its name states, it is a model-agnostic method: it does not require internal access to the model, so it can be applied to any trained machine learning model, including ensembles and deep networks treated as black boxes.

Its scope is deliberately narrow. LIME focuses on explaining a single prediction instance, generating local explanations for that specific case rather than characterizing the model as a whole. That makes it a natural fit for case-by-case review workflows—adverse action notices, clinical second opinions, fraud investigation—where the consumer of the explanation cares about one decision, not the population.

On cost, LIME sits in a middle position. A perspective paper comparing SHAP and LIME notes that LIME is generally faster than KernelSHAP, one of SHAP's common implementations, but it can still be computationally intensive for complex explanations. Its speed is relative and depends on the complexity of the local model and the data being perturbed. For teams that need many explanations across a large dataset, that per-instance cost multiplies quickly.

SHAP: A Unified Approach to Feature Attribution

SHAP is a post-hoc, model-agnostic method that can be applied to any machine learning model, according to the same comparative perspective paper. Its theoretical basis is game theory: it calculates the contribution of each player to the payout, with features standing in for players and the model outcome for the payout. The result is a score for each feature representing its weight on the model output—an additive feature attribution where explanations are represented as a linear model.

What distinguishes SHAP is coherence between scales. As the Interpretable Machine Learning book by Christoph Molnar explains, global interpretations are consistent with the local explanations because Shapley values are the "atomic unit" of the global interpretations. If you use LIME for local explanations and partial dependence plots plus permutation feature importance for global explanations, you lack a common foundation. SHAP also produces contrastive explanations that compare a prediction with the average prediction, which is often exactly the framing stakeholders want.

The trade-off is computational. Molnar is direct about the limitation: "KernelSHAP is slow. This makes KernelSHAP impractical to use when you want to compute Shapley values for many instances." All global SHAP methods, such as SHAP feature importance, require computing Shapley values for a lot of instances. There is an important exception: SHAP has a fast implementation for tree-based models, which Molnar credits as key to the method's popularity, since the biggest barrier to adopting Shapley values is otherwise slow computation. KernelSHAP also ignores feature dependence, a problem shared by most permutation-based interpretation methods.

Permutation Importance: Ranking Global Feature Impact

Permutation Feature Importance takes a direct operational approach to global explanation: it measures a feature's importance by observing how much model performance changes when that feature's values are permuted, breaking its relationship with the target. A feature whose shuffling degrades performance substantially is important; one whose shuffling changes little is not. The systematic review of XAI methods ranked it fourth in popularity among the techniques it surveyed.

Because it works purely through the model's prediction interface, it does not need to inspect the model's internal structure—it is grouped alongside LIME and PDPs among model-agnostic XAI techniques, and its operational definition implies that agnosticism even where a single passage does not state it explicitly. Its output answers the "what matters most" question: which variables most affect predictions overall.

Its cost profile follows from its mechanism. Each permutation requires additional model evaluations, so the computational cost can be significant for large datasets or complex models, since the method depends on multiple prediction passes per feature. This cost characterization is qualitative and inferred from how the method operates rather than from published benchmarks, which is a limitation worth keeping in mind when planning production deployments.

Partial Dependence Plots: Visualizing Feature Effects

Partial Dependence Plots answer a different global question than Permutation Importance. As teaching materials on explainable AI put it: while feature importance shows what variables most affect predictions, partial dependence plots show how a feature affects predictions. A typical question they answer is: controlling for all other house features, what impact do longitude and latitude have on home prices?

PDPs work by averaging model predictions over the values of other features, which requires no access to the model's internal parameters—another model-agnostic, global method. They are useful for visualizing the shape of a feature's effect, and the same materials note that on sophisticated models they can capture more complex patterns than coefficients from simple linear or logistic regression models, while remaining interpretable in a similar way.

Like Permutation Importance, their cost is driven by repeated model evaluations: generating the curve requires predicting across a grid of feature values while averaging over the data, which can be significant for large datasets or complex models. PDPs are best treated as a complement to a ranking method—importance tells you which features deserve scrutiny, and the dependence plot tells you what the relationship actually looks like.

XAI Method Decision Framework

The table below compares all four methods across the four dimensions that drive the selection decision: interpretability scope, model agnosticism, explanation type, and relative computational cost. Use it to match a method to your project's primary constraint before committing engineering effort.

Comparison of four post-hoc XAI methods across key selection criteria
Method	Interpretability Scope	Model Agnosticism	Explanation Type	Relative Computational Cost
LIME	Local — explains a single prediction instance	Yes — no internal model access required	Local explanations for a specific prediction	Generally faster than KernelSHAP, but can be intensive for complex explanations; scales poorly across many instances
SHAP	Both — local and global, consistent with each other	Yes — post-hoc, applicable to any model	Additive feature attributions, represented as a linear model; contrastive vs. average prediction	KernelSHAP is slow and impractical for many instances; fast implementation exists for tree-based models
Permutation Importance	Global — ranks features by impact on overall performance	Yes — operates through the prediction interface	Feature importance ranking	Multiple model evaluations per feature; can be significant for large datasets or complex models
Partial Dependence Plots	Global — shows how a feature affects predictions overall	Yes — averages predictions without internal parameters	Feature effect curves (marginal effect on prediction)	Requires predictions across a grid of values; can be significant for large datasets or complex models

Reading the table as a decision path: if your priority is explaining individual predictions, LIME is the lightweight option and SHAP is the coherent one. If your priority is overall model behavior, Permutation Importance answers "what matters" and PDPs answer "how does it matter." If you need both scales on a common foundation and can absorb the cost—or you have a tree-based model where SHAP's fast implementation applies—SHAP is the only method that delivers both. Note that the cost column is qualitative and relative throughout; none of these figures come from standardized benchmarking, and the Permutation Importance and PDP entries are inferred from their operational mechanisms.

Making an Informed XAI Choice

The decision, stated plainly: select an XAI method by prioritizing either local or global interpretability, considering the model's complexity and the computational resources available for explanation generation. Start with the question your stakeholders actually ask. If it is "why was this specific case decided this way," you need LIME or SHAP. If it is "how does this model behave overall," you need Permutation Importance, PDPs, or SHAP's global views. If it is both, SHAP is the only one of the four that provides them on a unified foundation—provided your compute budget or model type accommodates it.

Two caveats from the evidence deserve weight in enterprise settings. First, the systematic review notes theoretical limitations of SHAP and related model-agnostic methods, including their additive and causal assumptions, which are particularly critical in heterogeneous data. Second, that review found that while many studies provide computational evaluation of explanations, none included structured human-subject usability validation—so no method in this comparison comes with proof that stakeholders will actually understand its output.

The measurable indicator of a good choice is concrete: successful integration of the chosen XAI method into the ML pipeline, providing actionable insights that align with the project's interpretability goals and performance constraints. If the method's explanations answer the questions your business users and reviewers ask, and its runtime fits within your pipeline's budget, the selection is working. If either condition fails—explanations that don't map to decisions, or runtimes that can't scale to your instance volume—that is the signal to revisit the table and trade scope against cost.

Choosing Between LIME, SHAP, Permutation Importance, and Partial Dependence Plots: A Practical Comparison

Local vs. Global Interpretability: Understanding the Difference

LIME: Explaining Individual Predictions

SHAP: A Unified Approach to Feature Attribution

Permutation Importance: Ranking Global Feature Impact

Partial Dependence Plots: Visualizing Feature Effects

XAI Method Decision Framework

Making an Informed XAI Choice

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