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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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The Universe

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Dr. Rishabh Pathak

In the context of research methodology, the term "universe" refers to the total group of items or units that are of interest to the researcher and about which information is sought. Understanding the concept of the universe is fundamental in defining the scope of a research study and determining the population from which a sample will be drawn. Here is an explanation of the concept of the universe in research:


1.    Definition:

o    The universe, also known as the population, represents the entire group of elements or units that possess the characteristics under study. It includes all the individuals, objects, or events that meet the criteria for inclusion in the research. The universe can be finite or infinite, hypothetical or existent, depending on the nature of the study.

2.    Finite Universe:

o    A finite universe is one in which the total number of items or units is definite and known. For example, the population of a city, the number of employees in a company, or the students in a school are examples of finite universes. In a finite universe, researchers can theoretically enumerate all the elements, although it may not always be practical to do so.

3.    Infinite Universe:

o    An infinite universe is one in which the total number of items or units is uncertain and potentially limitless. Examples of infinite universes include the number of stars in the sky, the listeners of a radio program, or the possible outcomes of a random event. In an infinite universe, it is impossible to list or count all the elements, making sampling necessary for research purposes.

4.    Hypothetical vs. Existent Universe:

o    A hypothetical universe consists of items or units that are conceptual or imaginary in nature. For instance, tossing a coin or rolling a dice represent hypothetical universes where the outcomes are known but not physically present. In contrast, an existent universe comprises concrete objects or entities that actually exist in reality, such as the population of a country or the customers of a business.

5.    Role in Sampling:

o    The universe serves as the foundation for sampling in research. Researchers define the universe to establish the boundaries of the study and determine the target population from which a sample will be selected. The characteristics and diversity of the universe influence the sampling method, sample size, and generalizability of the study findings.

6.    Sampling Theory:

o    Sampling theory explores the relationship between the universe and the sample drawn from it. It provides a framework for selecting samples that are representative of the universe and for making statistical inferences about the population based on the sample data. Sampling theory is essential for ensuring the validity and reliability of research findings.

In summary, the universe in research methodology represents the total group of items or units that are the focus of a study. Understanding the nature of the universe, whether finite or infinite, hypothetical or existent, is crucial for designing sampling strategies, conducting data collection, and drawing meaningful conclusions in research.

 

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