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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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Unrestricted Sampling

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Dr. Rishabh Pathak

Unrestricted sampling, also known as simple random sampling, is a fundamental sampling technique where each element in the population has an equal and independent chance of being selected for the sample. In unrestricted sampling:


1.    Equal Probability of Selection:

§  In simple random sampling, every element in the population has an equal probability of being chosen for the sample. This ensures that each unit is selected independently of other units, without any bias towards specific elements.

2.    Random Selection:

§  The selection of sample elements is done randomly, without any systematic pattern or predetermined order. This randomness is essential to ensure that the sample is representative of the population and to minimize selection bias.

3.    Independence of Selection:

§  Each selection is made independently of previous selections, meaning that the inclusion or exclusion of one element does not influence the selection of other elements. This independence helps maintain the randomness of the sample.

4.    Statistical Validity:

§  Simple random sampling is a statistically valid method that allows researchers to make inferences about the population based on the characteristics of the sample. It provides a basis for estimating population parameters and assessing the precision of the results.

5.    Efficiency and Simplicity:

§  Unrestricted sampling is straightforward to implement and analyze, making it an efficient sampling method for many research studies. It does not require complex stratification or clustering procedures, which can simplify the sampling process.

6.    Representativeness:

§  When conducted properly, simple random sampling can produce a sample that is representative of the population, allowing researchers to generalize their findings with confidence. This representativeness is crucial for drawing valid conclusions from the sample data.

7.    Sampling Error:

§  Despite its advantages, simple random sampling may still be subject to sampling error, which is the variability between sample estimates and population parameters. Researchers should account for sampling error when interpreting the results of a simple random sample.

Overall, unrestricted sampling through simple random sampling is a foundational and widely used technique in research methodology. By ensuring randomness and equal probability of selection, researchers can create samples that are unbiased, representative, and suitable for making valid inferences about the population.

 

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