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

Mapping Lineage in The Developing Nervous System with Brainbow Multicolour Transgenes

Image
Dr. Rishabh Pathak

Mapping lineage in the developing nervous system with Brainbow multicolour transgenes involves a powerful genetic tool that enables the visualization and tracking of individual cells and their progeny with distinct fluorescent colors. Here are some key points related to mapping lineage in the developing nervous system using Brainbow multicolour transgenes:

1.      Principle of Brainbow Technology:

o  Genetic Mosaicism: Brainbow transgenes utilize combinatorial expression of fluorescent proteins to create a diverse color palette within individual cells, resulting in a unique color identity for each cell and its descendants.

o    Random Recombination: By incorporating multiple fluorescent protein variants and employing stochastic Cre-lox recombination events, Brainbow transgenes generate a spectrum of colors that can be used to label and trace cell lineages in a spatially and temporally controlled manner.

2.     Lineage Tracing in the Developing Nervous System:

o    Cell Fate Mapping: Brainbow multicolour transgenes allow for the precise labeling and visualization of individual cells and their progeny during neural development, facilitating the mapping of cell lineages, clonal relationships, and migration patterns in the developing nervous system.

o  Axon Tracing: In addition to lineage analysis, Brainbow technology can be used to trace axonal projections, synaptic connections, and neural circuits, providing insights into the wiring of the developing brain and the establishment of functional neuronal networks.

3.     Cellular Diversity and Connectivity:

o Neuronal Diversity: By labeling individual cells with distinct colors, Brainbow transgenes reveal the cellular diversity and heterogeneity within developing neural populations, highlighting the generation of different neuronal subtypes, glial cells, and neural progenitors during embryogenesis.

o    Synaptic Connectivity: Mapping lineage with Brainbow technology enables the visualization of synaptic connections between neurons, the formation of neural circuits, and the refinement of connectivity patterns essential for sensory processing, motor control, and cognitive functions in the developing nervous system.

4.    Functional Insights and Developmental Dynamics:

o Functional Analysis: Brainbow-mediated lineage mapping provides functional insights into cell fate decisions, proliferation dynamics, migration behaviors, and differentiation trajectories of neural progenitors and precursor cells during neurogenesis and gliogenesis in the developing brain.

o  Developmental Plasticity: By tracking individual cells over time and across brain regions, Brainbow transgenes offer a dynamic view of developmental plasticity, cellular interactions, and morphogenetic processes shaping the architecture and function of the nervous system during embryonic and postnatal stages.

In summary, mapping lineage in the developing nervous system with Brainbow multicolour transgenes offers a sophisticated approach to visualize, analyze, and understand the cellular diversity, connectivity, and developmental dynamics of neural populations during embryogenesis and early brain maturation. By leveraging the unique color-coding capabilities of Brainbow technology, researchers can unravel the complexities of neural development, circuit formation, and functional organization in the developing nervous system, advancing our knowledge of brain development and neurodevelopmental disorders.

 

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

Conducting a Qualitative Analysis

Dr. Rishabh Pathak
Conducting a qualitative analysis in biomechanics involves a systematic process of collecting, analyzing, and interpreting non-numerical data to gain insights into human movement patterns, behaviors, and interactions. Here are the key steps involved in conducting a qualitative analysis in biomechanics: 1.     Data Collection : o     Use appropriate data collection methods such as video recordings, observational notes, interviews, or focus groups to capture qualitative information about human movement. o     Ensure that data collection is conducted in a systematic and consistent manner to gather rich and detailed insights. 2.     Data Organization : o     Organize the collected qualitative data systematically, such as transcribing interviews, categorizing observational notes, or indexing video recordings for easy reference during analysis. o     Use qualitative data management tools or software to f...
Post a Comment
Read more