# Explainability and Interpretability in Concept and Data Drift: A Systematic Literature Review
## 2 Background: Drift Phenomena and Explainable/Interpretable AI
### Explainable AI
#### 2.3.2 Classical Approaches
The right-hand plot varies wind speed; rentals decline steadily up to about 25 km h\({}^{-1}\) and then appear to stabilize, but the rug marks along the x-axis reveal few training examples at higher wind speeds, reminding readers that the apparent plateau may be an artifact of sparse data. By presenting these three curves side by side, the PDP makes the random forest's global weather-related reasoning transparent without requiring inspection of individual trees [21]. * **Accumulated Local Effects (ALE)**: This method improves on the PDP when features are correlated. The feature range is partitioned into data-driven intervals, local gradients are computed within each slice using only observed data points, and these gradients are accumulated into a centered curve. ALE therefore avoids the extrapolation bias of PDPs and remains computationally light. It is a global explanation method. Figure 8: LIME example. Figure 9: PDP example. Figure 10 illustrates the idea with a random forest model that predicts the number of bikes rented each day from weather variables. In the left panel, the ALE curve for wind speed dips steadily below zero, showing that stronger winds depress rental counts; the rug at the bottom reminds the reader that very high wind values are rare, so the slight uptick beyond 30 km h\({}^{-1}\) is probably noise rather than a true reversal. The right panel reports the categorical main effect for the \"weather situation\": relative to the baseline of good weather, misty conditions reduce demand modestly, whereas bad weather slashes expected rentals by more than six hundred bikes--both findings align with common sense. Because ALE can also be extended to two-way interaction surfaces, analysts can inspect, for example, how humidity modifies the temperature effect without re-plotting the main trends; the interaction plot shows only the additional joint influence, leaving the main effects undisturbed and easy to read [21]. * **Permutation Importance (PI)**: This quantifies how strongly a model depends on each input variable by randomly shuffling that variable in a validation set, keeping the model weights fixed, and measuring how much the chosen error metric worsens. If performance metrics like mean absolute error or accuracy remain stable after permutation, the feature is redundant; substantial drops indicate critical importance. It offers a purely global view of importance. Figure 11 makes this idea concrete for a support vector machine regressor that predicts daily bike rental counts from weather and calendar information. After every feature is permuted 100 times, the drop in mean absolute error is averaged (black dot) and a 5 th-95 th-percentile range is plotted as a horizontal bar. The variable cnt_2d_bfr--the number of bikes rented two days before--causes by far the largest performance loss and therefore tops the ranking, followed by temperature, humidity, and wind speed. Calendar variables such as holidays contribute little: shuffling their values barely nudges the error. Reading the plot is simple: the farther a dot lies to the right and the longer its bar, the more the model would suffer if that feature were unreliable, providing an intuitive yard-stick for novices who have never inspected raw support vector weights [21]. * **Surrogate Models**: Training proceeds by fitting an interpretable model--not to the true labels but to the predictions of a black box--so that the surrogate reproduces the original decision surface as closely as possible. The resulting tree offers a global sketch of the black box, while any root-to-leaf path can be read as a local, rule-based explanation. Figure 10: ALE example. Figure 12 shows a CART surrogate that mimics a support vector machine (SVM) trained to forecast daily bike rental counts from weather and calendar features. Using the original training data, the tree is fitted to the SVM's outputs and attains an \(R^{2}=0.76\) on hold-out cases, indicating a good--though not perfect--approximation of the black-box behavior.