Interpretable machine learning builds models and workflows that help people understand what drives predictions, validate assumptions, and diagnose failures. It supports trust, compliance, safety, and faster iteration. Practitioners combine global views that summarize model behavior with local views that explain a single prediction. Techniques span model agnostic tools, visualization, and architecture choices. This article surveys the Top 10 Interpretability Techniques for ML Practitioners so beginners and experts can choose suitable methods for tabular, text, vision, and time series. The aim is practical guidance on when to use each method, how to avoid pitfalls, and how to mix them for robust, human centered explanations.
#1 SHAP and Shapley values
SHAP based on Shapley values distributes a prediction among input features using cooperative game theory. It provides both local and global explanations with consistency, additivity, and contrastive reasoning. Summary plots reveal dominant drivers and interaction effects, while dependence plots show how a feature’s contribution varies across its range. Use TreeSHAP for boosted trees, KernelSHAP for any model, and DeepSHAP for neural networks. Strong practices include background data selection, grouping correlated variables, and checking stability across cross validation folds. Beware leakage, extrapolation outside the training manifold, and confusing correlation with causation when interpreting high magnitude attributions.
#2 LIME local surrogates
LIME explains an individual prediction by sampling points around the instance, querying the black box, and fitting a sparse linear model that approximates local behavior. It is model agnostic and fast, which makes it useful for quick debugging and user facing explanations. You can tune kernel width, number of samples, and feature selection to balance faithfulness and readability. Stability checks include repeating runs with different seeds and assessing agreement with SHAP or counterfactuals. Limitations include sensitivity to sampling, difficulty with strong interactions, and the risk of misleading explanations when the local linear surrogate fails to capture the decision boundary.
#3 Permutation importance
Permutation importance measures a feature’s global contribution by shuffling its values and recording the drop in a chosen metric. It is simple, model agnostic, and works for regression and classification. Compute importances on a proper validation set to avoid bias, and repeat over multiple shuffles to estimate variance. To handle collinearity, compute conditional permutation importance or group related variables and permute them together. Contrast with split gain based importances that can be biased by cardinality or missing value handling. Use the results to prune features, guide data collection, and prioritize deeper analysis with partial dependence or SHAP.
#4 Partial dependence and ICE
Partial dependence plots show the marginal effect of a feature on predictions by averaging model outputs over the data while varying that feature. Individual conditional expectation curves add per instance trajectories, revealing heterogeneous responses that the average may hide. Both tools illuminate nonlinearity, thresholds, and saturation, and they pair well with SHAP dependence plots. Use centered ICE to compare relative changes and compute two way partial dependence to uncover interactions. Be cautious with extrapolation, especially when varying features far from the observed joint distribution. Mitigate by clustering ICE curves, restricting ranges to dense regions, and validating insights on held out data.
#5 Counterfactual explanations
Counterfactual explanations answer the question of what minimal changes would switch a prediction to a desired outcome. They are intuitive for stakeholders because they propose actionable edits such as lowering debt to income or increasing tenure. Quality counterfactuals must be sparse, plausible, and cost aware, using constraints to respect domain rules and monotonicity. Techniques include gradient based search for differentiable models, heuristic search for trees, and generative models to stay on the data manifold. Evaluate validity, proximity, diversity, and feasibility metrics. Use multiple counterfactuals per case to broaden options, and discuss ethical concerns to avoid suggesting unfair or unsafe interventions.
#6 Saliency, integrated gradients, and Grad CAM
Saliency methods attribute importance to input pixels or tokens in deep networks. Gradient magnitude maps, integrated gradients, and SmoothGrad highlight sensitive regions in images or words in text classifiers. Grad CAM produces coarse localization maps for convolutional networks by weighting activation maps with class specific gradients. Good practice includes sanity checks such as randomizing weights to confirm explanations degrade, and evaluating faithfulness with deletion and insertion tests. Stabilize results by averaging over augmentations and using noise smoothing. Limitations include susceptibility to gradient saturation and resolution constraints. Combine with bounding boxes, captions, or concept probes to communicate insights to non technical users.
#7 Surrogate models for global and local views
Surrogate modeling fits an interpretable model that mimics a complex predictor in a region of interest or globally. Common surrogates are sparse linear models, shallow decision trees, rule lists, and generalized additive models. Train the surrogate on inputs paired with black box predictions, then assess fidelity with held out samples and local error maps. Use global surrogates for policy and compliance summaries, and local surrogates for case explanations and debugging. Beware oversimplification that hides interactions or non monotonic effects. Report both surrogate accuracy and original model accuracy, and highlight regions where the surrogate is unreliable to avoid false confidence.
#8 Rule based explanations and Anchors
Rule based explanations communicate decisions as human readable if then patterns with precision guarantees. Anchors is a popular method that searches for high precision rules that fix a prediction when satisfied, making the scope and limits of the explanation explicit. Such rules support audits, eligibility policies, and explanations for regulated decisions. Training involves sampling to test candidate conditions and selecting concise rule sets with coverage and stability regularization. Evaluate precision, coverage, and overlap, and prefer features that users can act on. Limitations include combinatorial search cost and brittleness under distribution shift. Pair with counterfactuals and partial dependence to provide both rules and what if guidance.
#9 Causal interpretability and treatment effects
Causal interpretability aims to answer how outcomes would change under interventions, not just associations. Techniques include causal graphs, adjustment using backdoor criteria, uplift modeling, and heterogeneous treatment effect estimation with meta learners or causal forests. These tools disentangle confounders, mediators, and colliders, clarifying which features truly influence outcomes. They guide policy by predicting effects of actionable levers rather than correlates. Key steps include identifying assumptions, testing sensitivity to unobserved confounding, and validating with natural experiments or A B tests. Causal reasoning complements attribution methods by preventing misleading narratives when spurious correlations create high importance for non actionable proxies.
#10 Concept based methods, TCAV, and prototypes
Concept based interpretability connects model internals to user defined concepts rather than raw features. TCAV learns linear directions in activation space that correspond to concepts like stripes or wheels, then measures how sensitive predictions are to those concepts. Prototype and criticism networks explain by reference to representative training examples and contrasting edge cases. These approaches are compelling for vision and audio, where human concepts align with recognizable patterns. Careful concept selection, validation with counterexamples, and tests for spurious cues are crucial. Use concept scores to guide data curation, bias audits, and to build narratives that resonate with domain experts and lay audiences.