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ICE Plot

Visualization showing how the prediction changes for each individual observation when a feature varies, unlike PDP which shows the average effect. ICE plots reveal heterogeneity of effects and subgroups of behavior.

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ALE Plot

Alternative to PDP that calculates local effects by averaging prediction differences in feature space regions. ALE plots avoid biases of PDP when features are correlated.

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Activation Maps

Heatmaps overlaid on input images showing regions that most strongly activate neurons in convolutional neural networks. Activation maps visually explain what the model detected in an image.

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t-SNE Visualization

Non-linear projection of high-dimensional data to 2D or 3D preserving local relationships between points to reveal clusters. t-SNE helps understand the latent structure of representations learned by the model.

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UMAP Projection

Dimensionality reduction technique alternative to t-SNE offering better preservation of global structure and faster execution speed. UMAP reveals underlying manifolds in embeddings of complex models.

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Model Comparison Radar Chart

Spider web visualization simultaneously comparing multiple models according to different performance metrics. This chart facilitates multi-criteria evaluation and selection of the best model according to priorities.

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Permutation Importance Visualization

Graph showing the performance drop of the model when values of a feature are randomly permuted, thus evaluating its real importance. This method captures non-linear effects and interactions better than classical importance.

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