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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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Different types of Simple Random Sampling

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Dr. Rishabh Pathak

Simple random sampling is a fundamental sampling technique where each element in the population has an equal probability of being selected for the sample. It is considered one of the most straightforward and unbiased sampling methods. Here are some different types of simple random sampling techniques:


1.    Lottery Method:

§  In the lottery method, each element in the population is assigned a unique number, and a random selection process (e.g., drawing numbers from a hat or using a random number generator) is used to choose the sample. This method ensures that every element has an equal chance of being selected.

2.    Random Number Table:

§  Researchers can use random number tables to select a sample from a population. In this method, each element is assigned a number, and random numbers from the table are used to choose the sample. By following a systematic pattern in the random number table, researchers can ensure randomness in the selection process.

3.    Random Sampling Software:

§  With advancements in technology, researchers can use random sampling software or computer programs to generate random samples from a population. These software tools use algorithms to ensure randomness and eliminate human bias in the selection process. Researchers can input the population data and specify the sample size to obtain a simple random sample.

4.    Simple Random Sampling Without Replacement:

§  In simple random sampling without replacement, once an element is selected for the sample, it is not returned to the population. This ensures that each element is selected only once in the sample, maintaining the randomness of the selection process. This method is commonly used in research studies to avoid duplication of elements in the sample.

5.    Table of Random Digits:

§  A table of random digits is a tool used in simple random sampling to select elements from a population. Researchers can start at a random point in the table and follow a predetermined pattern to choose sample elements. By using a table of random digits, researchers can achieve randomness and avoid bias in the selection process.

6.    Physical Randomization:

§  Physical randomization techniques involve physically mixing or shuffling elements in the population to ensure randomness in sample selection. For example, placing numbered balls in a container and drawing them one by one can be a form of physical randomization to select a sample. This method is simple and transparent, ensuring fairness in the selection process.

Simple random sampling techniques provide researchers with a straightforward and unbiased way to select samples from populations. By employing different methods such as the lottery method, random number tables, and random sampling software, researchers can ensure that each element in the population has an equal chance of being included in the sample, leading to representative and reliable research outcomes.

 


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