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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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What is Python?

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Dr. Rishabh Pathak

Python is a high-level, general-purpose programming language that has become widely popular, especially in data science and machine learning fields. According to the book, Python combines the power of general-purpose programming languages with the ease of use of domain-specific scripting languages like MATLAB or R. It offers a vast ecosystem of libraries and tools for data loading, visualization, statistics, natural language processing, image processing, and more.

Key points about Python:

  • Ease of Use: Python is known for its readable and concise syntax, which makes it accessible for beginners and efficient for experts.
  • Wide Range of Libraries: Its extensive standard and third-party libraries provide functionality that supports various stages of machine learning workflows — from data manipulation (pandas, NumPy) to visualization (matplotlib) and model building (scikit-learn).
  • Interactivity: Python supports interactive computing environments such as Jupyter Notebook, allowing an iterative approach to data analysis and machine learning.
  • Versatility: Beyond data science, Python can be used to build complex graphical user interfaces, web services, and integrate with other systems.
  • Open Source: Python is free and open source, encouraging community contributions and wide adoption.

Overall, Python is considered the lingua franca of data science and machine learning because it balances general programming power with domain-specific features and an easy-to-use interface.

 

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