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Uncertainty in Multiclass Classification

Dr. Rishabh Pathak
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 ...
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Changes in the Brain can be shown at many levels of analysis

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Dr. Rishabh Pathak

Changes in the brain can be observed and studied at various levels of analysis, providing insights into the mechanisms underlying brain plasticity and behavior. Here are different levels of analysis where changes in the brain can be demonstrated:


1.     Behavioral Changes: Behavioral changes are often the most visible indicators of brain plasticity. Alterations in behavior, such as learning new skills, adapting to new environments, or responding to stimuli, reflect underlying changes in neural circuits and synaptic connections.


2.  Global Measures of Brain Activity: Techniques such as functional magnetic resonance imaging (fMRI), positron emission tomography (PET), and electroencephalography (EEG) allow researchers to observe changes in brain activity at a macroscopic level. These imaging methods provide insights into overall brain function and connectivity.


3.  Synaptic Changes: Synaptic plasticity plays a crucial role in learning and memory processes. Changes in synaptic strength, formation of new synapses, and pruning of existing synapses can be studied at the level of individual synapses to understand how neural networks adapt to experiences.


4.    Molecular Processes: Molecular changes within neurons, such as modifications in ion channels, gene expression, and protein synthesis, underlie synaptic plasticity and long-term changes in brain function. Studying molecular processes provides a detailed understanding of the cellular mechanisms driving brain plasticity.


5.  Anatomical Changes: Structural changes in the brain, including alterations in neuronal morphology, dendritic arborization, and axonal growth, can be visualized using techniques like electron microscopy and immunohistochemistry. Anatomical changes reflect the structural reorganization of neural circuits in response to experiences.


6.  Physiological Changes: Physiological measures, such as changes in neuronal excitability, neurotransmitter release, and synaptic transmission, offer insights into the functional adaptations of the brain. Studying physiological changes helps link cellular processes to behavioral outcomes.


By examining changes in the brain at multiple levels of analysis, researchers can gain a comprehensive understanding of how neural plasticity shapes behavior and cognition. Integrating findings from different levels of analysis provides a holistic view of brain function and adaptation to environmental stimuli.

 

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