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A linear probe is a simple linear model (logistic or ridge regression) trained on a frozen model's embeddings to test how much useful information those embeddings hold.
Logistic regression models the log-odds of an outcome as a linear combination of inputs (Wikipedia); trained on frozen embeddings it becomes a probe of the representation, since whatever it can predict must be linearly readable from the features. Pathology foundation-model papers report linear-probe accuracy across many tasks as their headline comparison, and frozen embedding, then PCA to a few hundred dimensions, then a linear head is the standard extraction recipe for transcriptome models too.
Shares Ridge regression and the multilayer perceptron, Embedding (learned representation), Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Fine-tuning versus frozen features, and LoRA, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Ridge regression and the multilayer perceptron, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Fine-tuning versus frozen features, and LoRA, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Embedding (learned representation), Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Logistic regression and nearest-centroid classifiers, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Ridge regression and the multilayer perceptron, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Embedding (learned representation), Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.