1. What is Uncertainty in Classification? Uncertainty refers to the model’s confidence or doubt in its predictions. Quantifying uncertainty is important to understand how reliable each prediction is. In multiclass classification , uncertainty estimates provide probabilities over multiple classes, reflecting how sure the model is about each possible class. 2. Methods to Estimate Uncertainty in Multiclass Classification Most multiclass classifiers provide methods such as: predict_proba: Returns a probability distribution across all classes. decision_function: Returns scores or margins for each class (sometimes called raw or uncalibrated confidence scores). The probability distribution from predict_proba captures the uncertainty by assigning a probability to each class. 3. Shape and Interpretation of predict_proba in Multiclass Output shape: (n_samples, n_classes) Each row corresponds to the probabilities of ...
A connectome is a comprehensive map of neural connections in the brain, representing the intricate network of structural and functional pathways that facilitate communication between different brain regions. 1. Definition: - A connectome is a detailed representation of the wiring diagram of the brain, illustrating the complex network of axonal projections, synaptic connections, and communication pathways between neurons and brain regions. - The connectome encompasses both the structural connectivity, which refers to the physical links between neurons and brain areas, and the functional connectivity, which reflects the patterns of neural activity and information flow within the brain. 2. Structural Connectome: - The structural connectome provides a map of the anatomical connections in the brain, showing how neurons are physically linked through axonal projections, white matter pathways, and synaptic contacts. - Techniques ...