Skip to main content

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 ...
Post a Comment

Supplementary Motor Area (SMA)

Image
Dr. Rishabh Pathak

The Supplementary Motor Area (SMA) is a region of the cerebral cortex that plays a crucial role in the planning, initiation, and coordination of voluntary movements. Here is an overview of the Supplementary Motor Area (SMA):


1.      Location:

oThe Supplementary Motor Area is located in the medial surface of the frontal lobe, anterior to the primary motor cortex (M1), and is part of the premotor cortex. It is situated bilaterally in the superior frontal gyrus.

2.     Function:

oThe SMA is involved in the planning and coordination of complex movements, especially sequences of movements and bilateral movements. It plays a role in the preparation and organization of motor actions before their execution.

3.     Motor Planning:

oThe SMA is particularly important for the internal generation of movements, such as those involved in tasks that require motor planning without external cues. It is involved in the coordination of movements based on internal representations of actions.

4.    Bilateral Movements:

oThe SMA is known to be involved in the coordination of bilateral movements, where both sides of the body need to work together in a synchronized manner. It helps in synchronizing movements between the two sides of the body.

5.     Role in Motor Learning:

oThe SMA is also implicated in motor learning processes. It is involved in the acquisition of new motor skills and the consolidation of motor memory. Damage to the SMA can lead to difficulties in learning new motor tasks.

6.    Connections:

oThe SMA has extensive connections with other motor areas of the brain, including the primary motor cortex, premotor cortex, basal ganglia, and cerebellum. These connections allow for the integration of motor planning and execution processes.

7.     Clinical Implications:

o Dysfunction of the SMA has been associated with movement disorders such as apraxia, where individuals have difficulty planning and executing purposeful movements. It is also implicated in conditions like Parkinson's disease and epilepsy.

8.    Research and Stimulation:

oThe SMA is a target for research using techniques like transcranial magnetic stimulation (TMS) to study its role in motor control and movement preparation. Stimulation of the SMA has been explored as a potential therapeutic approach in movement disorders.

In summary, the Supplementary Motor Area (SMA) is a critical region of the brain involved in motor planning, coordination of complex movements, and the internal generation of actions. Its functions extend to bilateral movements, motor learning, and the integration of motor processes. Understanding the role of the SMA provides insights into motor control mechanisms and neurological conditions affecting movement coordination.

 

Categories:

Comments

Post a Comment

Popular posts from this blog

Relation of Model Complexity to Dataset Size

Dr. Rishabh Pathak
Core Concept The relationship between model complexity and dataset size is fundamental in supervised learning, affecting how well a model can learn and generalize. Model complexity refers to the capacity or flexibility of the model to fit a wide variety of functions. Dataset size refers to the number and diversity of training samples available for learning. Key Points 1. Larger Datasets Allow for More Complex Models When your dataset contains more varied data points , you can afford to use more complex models without overfitting. More data points mean more information and variety, enabling the model to learn detailed patterns without fitting noise. Quote from the book: "Relation of Model Complexity to Dataset Size. It’s important to note that model complexity is intimately tied to the variation of inputs contained in your training dataset: the larger variety of data points your dataset contains, the more complex a model you can use without overfitting....
Post a Comment
Read more

Linear Models

Dr. Rishabh Pathak
1. What are Linear Models? Linear models are a class of models that make predictions using a linear function of the input features. The prediction is computed as a weighted sum of the input features plus a bias term. They have been extensively studied over more than a century and remain widely used due to their simplicity, interpretability, and effectiveness in many scenarios. 2. Mathematical Formulation For regression , the general form of a linear model's prediction is: y^ ​ = w0 ​ x0 ​ + w1 ​ x1 ​ + … + wp ​ xp ​ + b where; y^ ​ is the predicted output, xi ​ is the i-th input feature, wi ​ is the learned weight coefficient for feature xi ​ , b is the intercept (bias term), p is the number of features. In vector form: y^ ​ = wTx + b where w = ( w0 ​ , w1 ​ , ... , wp ​ ) and x = ( x0 ​ , x1 ​ , ... , xp ​ ) . 3. Interpretation and Intuition The prediction is a linear combination of features — each feature contributes prop...
Post a Comment
Read more

Predicting Probabilities

Dr. Rishabh Pathak
1. What is Predicting Probabilities? The predict_proba method estimates the probability that a given input belongs to each class. It returns values in the range [0, 1] , representing the model's confidence as probabilities. The sum of predicted probabilities across all classes for a sample is always 1 (i.e., they form a valid probability distribution). 2. Output Shape of predict_proba For binary classification , the shape of the output is (n_samples, 2) : Column 0: Probability of the sample belonging to the negative class. Column 1: Probability of the sample belonging to the positive class. For multiclass classification , the shape is (n_samples, n_classes) , with each column corresponding to the probability of the sample belonging to that class. 3. Interpretation of predict_proba Output The probability reflects how confidently the model believes a data point belongs to each class. For example, in ...
Post a Comment
Read more

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 ...
Post a Comment
Read more

LPFC Functions

Dr. Rishabh Pathak
The lateral prefrontal cortex (LPFC) plays a crucial role in various cognitive functions, particularly those related to executive control, working memory, decision-making, and goal-directed behavior. Here are key functions associated with the lateral prefrontal cortex: 1.      Executive Functions : o     The LPFC is central to executive functions, which encompass higher-order cognitive processes involved in goal setting, planning, problem-solving, cognitive flexibility, and inhibitory control. o     It is responsible for coordinating and regulating other brain regions to support complex cognitive tasks, such as task switching, attentional control, and response inhibition, essential for adaptive behavior in changing environments. 2.      Working Memory : o     The LPFC is critical for working memory processes, which involve the temporary storage and manipulation of information to guide behavior and decis...
Post a Comment
Read more