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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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Ex Post Facto Research Design

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Dr. Rishabh Pathak

Ex post facto research design, also known as causal-comparative research, is a type of non-experimental research design that examines the relationship between independent and dependent variables after they have occurred naturally. In ex post facto studies, the researcher does not manipulate the independent variable but rather observes and analyzes the existing differences or relationships between variables. Here are key characteristics and components of ex post facto research design:


1. No Manipulation of Variables: In ex post facto research, the researcher does not have control over the independent variable. The independent variable has already occurred or been naturally assigned, and the researcher observes the effects or relationships with the dependent variable after the fact.


2.    Retrospective Analysis: Ex post facto research involves a retrospective analysis of existing data or conditions. Researchers analyze data that have already been collected or events that have already taken place to investigate possible causal relationships between variables.


3.    Causal-Comparative Analysis: Ex post facto research aims to compare groups or conditions that differ on the independent variable to determine the effects on the dependent variable. The researcher seeks to establish causal relationships or associations based on observed differences or correlations.


4.    Identifying Causal Relationships: While ex post facto research cannot establish causation definitively due to the lack of experimental control, it can provide valuable insights into potential causal relationships between variables. Researchers may use statistical analyses to explore the relationships and draw inferences based on the observed patterns.


5.    Control of Extraneous Variables: Researchers in ex post facto studies must consider and control for extraneous variables that could influence the relationship between the independent and dependent variables. Statistical techniques such as regression analysis or analysis of covariance may be used to account for these variables.


6.    Cross-Sectional or Longitudinal Design: Ex post facto research can utilize cross-sectional or longitudinal designs to examine relationships between variables at a specific point in time or over a period. Longitudinal studies allow researchers to track changes and trends in variables over time.


7.    Applications: Ex post facto research is commonly used in educational research, social sciences, and healthcare to investigate the effects of variables that cannot be manipulated for ethical or practical reasons. For example, studying the impact of gender on academic achievement or the relationship between socioeconomic status and health outcomes.


8.    Limitations: One of the main limitations of ex post facto research is the inability to establish causation definitively due to the lack of experimental control. Researchers must be cautious in interpreting the results and consider alternative explanations for the observed relationships.


Ex post facto research design provides a valuable approach for exploring causal relationships between variables in situations where experimental manipulation is not feasible or ethical. By analyzing existing data and conditions, researchers can gain insights into potential causal links and contribute to the understanding of complex phenomena in various fields of study.

 

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