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

Bilateral Independent Periodic Epileptiform Discharges Compared to Triphasic Patterns

Image
Dr. Rishabh Pathak

Bilateral Independent Periodic Epileptiform Discharges (BIPLEDs) and triphasic patterns are both important EEG findings that indicate different underlying neurological conditions. 

Bilateral Independent Periodic Epileptiform Discharges (BIPLEDs)

1.      Definition:

§  BIPLEDs are characterized by periodic discharges that are independent and asynchronous across both hemispheres. They can occur in various forms and are distinguished from other types of periodic discharges.

2.     Clinical Significance:

§  BIPLEDs are often associated with severe diffuse cerebral dysfunction, such as in cases of encephalopathy, infections, or neurodegenerative diseases. They indicate significant underlying pathology and are generally associated with a poor prognosis.

3.     EEG Characteristics:

§  BIPLEDs typically show regular, periodic discharges that can vary in amplitude and duration. The waveforms may be sharp or slow, and there is often a low-amplitude background activity between discharges. The intervals between discharges tend to be consistent.

4.    Etiologies:

§  Common causes include metabolic disturbances, toxic exposures, infectious processes (like encephalitis), and severe brain injuries. BIPLEDs can also be seen in postictal states and in conditions like Creutzfeldt-Jakob disease.

5.     Prognosis:

§  The presence of BIPLEDs is generally associated with a worse prognosis compared to other EEG patterns, indicating significant brain dysfunction and a higher likelihood of poor neurological outcomes.

Triphasic Patterns

6.    Definition:

§  Triphasic patterns are characterized by a specific waveform that consists of three phases: an initial positive deflection, a negative deflection, and a final positive deflection. These patterns are typically seen in a more synchronized manner across the hemispheres.

7.     Clinical Significance:

§  Triphasic patterns are often associated with metabolic disturbances, particularly in cases of hepatic encephalopathy, uremic encephalopathy, and other reversible metabolic conditions. They are generally considered to have a better prognosis than BIPLEDs when associated with reversible causes.

8.    EEG Characteristics:

§  The triphasic waveform is typically maximal in the frontal regions and may show a characteristic anterior-to-posterior lag. The intervals between the individual waves in a triphasic pattern are inconsistent, contrasting with the periodicity seen in BIPLEDs.

9.    Etiologies:

§  Common causes of triphasic patterns include metabolic disturbances, particularly those related to liver or kidney failure, and can also be seen in cases of drug intoxication or other reversible conditions.

10.                        Prognosis:

§  The prognosis associated with triphasic patterns can be more favorable, especially if the underlying cause is reversible. However, if associated with severe brain injury or chronic conditions, the prognosis may be poor.

Summary of Differences

Feature

BIPLEDs

Triphasic Patterns

Definition

Periodic, asynchronous discharges

Specific three-phase waveform

Clinical Significance

Indicates severe diffuse cerebral dysfunction

Often associated with metabolic disturbances

EEG Characteristics

Regular, periodic discharges

Characteristic triphasic waveform

Etiologies

Metabolic, infectious, neurodegenerative

Metabolic disturbances, particularly hepatic

Prognosis

Generally poor prognosis

Variable prognosis, often better if reversible

 

Conclusion

Both BIPLEDs and triphasic patterns are critical EEG findings that reflect significant brain dysfunction. While BIPLEDs indicate diffuse cerebral issues often associated with poor outcomes, triphasic patterns are typically linked to metabolic disturbances and may have a more favorable prognosis when the underlying cause is reversible. Understanding these differences is essential for clinicians in diagnosing and managing patients with neurological conditions.

 

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

Kernelized Support Vector Machines

Dr. Rishabh Pathak
1. Introduction to SVMs Support Vector Machines (SVMs) are supervised learning algorithms primarily used for classification (and regression with SVR). They aim to find the optimal separating hyperplane that maximizes the margin between classes for linearly separable data. Basic (linear) SVMs operate in the original feature space, producing linear decision boundaries. 2. Limitations of Linear SVMs Linear SVMs have limited flexibility as their decision boundaries are hyperplanes. Many real-world problems require more complex, non-linear decision boundaries that linear SVM cannot provide. 3. Kernel Trick: Overcoming Non-linearity To allow non-linear decision boundaries, SVMs exploit the kernel trick . The kernel trick implicitly maps input data into a higher-dimensional feature space where linear separation might be possible, without explicitly performing the costly mapping . How the Kernel Trick Works: Instead of computing ...
Post a Comment
Read more

Supervised Learning

Dr. Rishabh Pathak
What is Supervised Learning? ·     Definition: Supervised learning involves training a model on a labeled dataset, where the input data (features) are paired with the correct output (labels). The model learns to map inputs to outputs and can predict labels for unseen input data. ·     Goal: To learn a function that generalizes well from training data to accurately predict labels for new data. ·          Types: ·          Classification: Predicting categorical labels (e.g., classifying iris flowers into species). ·          Regression: Predicting continuous values (e.g., predicting house prices). Key Concepts: ·     Generalization: The ability of a model to perform well on previously unseen data, not just the training data. ·         Overfitting and Underfitting: ·    ...
Post a Comment
Read more