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Partial dependence describes an average model response when one or more features are set to chosen values. For a single feature, the method takes each evaluation example, substitutes the same selected feature value, obtains a prediction, then averages across examples. Repeating this operation over a grid produces a curve. It is a view of the fitted model, not a causal estimate of what would happen if a person or system were intervened upon. The average can be useful when asking how predictions vary across a population, but it may hide important heterogeneity. Individual conditional expectation, or ICE, draws a separate line for each example while the feature changes. If lines remain parallel, effects may be similar in shape; if they spread, cross, or bend differently, the average conceals variation. Centered ICE subtracts each line's prediction at a reference point to make differences in shape easier to see. A critical limitation arises when the selected feature is correlated with other inputs. The calculation holds the other features fixed while replacing one value, which can create combinations absent from the data. A model may then be queried far outside realistic support. For example, increasing a person's age while holding a related education or career history constant may produce implausible examples. The average can be misleading even when computed correctly. Inspect data distributions and feature dependence alongside plots. Restrict the grid to observed ranges, look for sparse regions, and consider stratified or conditional alternatives. Accumulated local effects summarize local changes over regions supported by data and can reduce some extrapolation concerns, though they answer a different question. Use domain knowledge to decide whether the feature combinations make sense. A plot should state which model output is shown, such as a probability or raw score, the evaluated sample, and the range of feature values. Always read the visual as model behavior under a specific calculation, not proof of mechanism or fairness.