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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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Mixed Sampling

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Dr. Rishabh Pathak

Mixed sampling, also known as combined sampling, refers to the use of multiple sampling methods within the same study or research project. Here are some key points about mixed sampling:


1.    Definition:

o   Mixed sampling involves the combination of different sampling techniques, such as random sampling, stratified sampling, cluster sampling, purposive sampling, etc., in a single study.

o    Researchers may use mixed sampling to capitalize on the strengths of different sampling methods and address specific research objectives or constraints.

2.    Purpose:

o   The primary goal of mixed sampling is to enhance the overall sampling strategy by leveraging the advantages of various sampling methods while mitigating their individual limitations.

o Researchers may use mixed sampling to improve the representativeness of the sample, increase the efficiency of data collection, or address specific population characteristics.

3.    Implementation:

o Researchers can employ mixed sampling by applying different sampling methods to different subgroups or stages of the study.

o For example, a study may use random sampling to select participants from a general population but then use purposive sampling to select specific subgroups for in-depth interviews.

4.    Advantages:

o    Allows researchers to tailor the sampling strategy to the specific research objectives and characteristics of the population.

o    Can improve the overall representativeness of the sample by combining different sampling methods.

o    Provides flexibility in sampling design, enabling researchers to address diverse research questions within the same study.

5.    Considerations:

o  Researchers must carefully plan and justify the use of mixed sampling methods based on the research objectives, population characteristics, and constraints.

o    Clear documentation of the sampling procedures and rationale for using mixed sampling is essential for transparency and reproducibility.

6.    Applications:

o  Mixed sampling is commonly used in social science research, market research, public health studies, and other fields where complex sampling strategies are needed.

o   It can be particularly useful when studying populations with diverse characteristics or when aiming to achieve a balance between representativeness and efficiency.

7.    Advantages over Single Sampling Methods:

o    Mixed sampling allows researchers to overcome the limitations of individual sampling methods by combining their strengths.

o    It can lead to a more comprehensive and nuanced understanding of the research topic by incorporating multiple perspectives and sampling approaches.

Mixed sampling offers researchers a flexible and adaptive approach to sampling, enabling them to optimize the sampling strategy based on the specific requirements of the study. By combining different sampling methods strategically, researchers can enhance the quality and depth of their research findings while addressing the complexities of diverse populations and research objectives.

 

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