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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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Prerequisite Knowledge for a Quantitative Analysis

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Dr. Rishabh Pathak

To conduct a quantitative analysis in biomechanics, researchers and practitioners require a solid foundation in various key areas. Here are some prerequisite knowledge areas essential for performing quantitative analysis in biomechanics:


1.    Anatomy and Physiology:

o    Understanding the structure and function of the human body, including bones, muscles, joints, and organs, is crucial for biomechanical analysis.

o    Knowledge of anatomical terminology, muscle actions, joint movements, and physiological processes provides the basis for analyzing human movement.

2.    Physics:

o    Knowledge of classical mechanics, including concepts of force, motion, energy, and momentum, is fundamental for understanding the principles underlying biomechanical analysis.

o    Understanding Newton's laws of motion, principles of equilibrium, and concepts of work, energy, and power is essential for quantifying and interpreting human movement.

3.    Mathematics:

o    Proficiency in mathematics, including algebra, trigonometry, calculus, and linear algebra, is necessary for formulating and solving biomechanical equations.

o    Mathematical modeling, data analysis, and statistical methods are essential for quantifying biomechanical variables, analyzing motion data, and interpreting results.

4.    Biomechanical Principles:

o    Knowledge of biomechanical principles, such as kinetics (forces and torques) and kinematics (motion and displacement), is essential for analyzing human movement quantitatively.

o    Understanding concepts like center of mass, moment of inertia, angular velocity, and acceleration is crucial for characterizing and interpreting biomechanical data.

5.    Instrumentation and Technology:

o Familiarity with biomechanical measurement tools and technologies, such as motion capture systems, force plates, electromyography (EMG), and dynamometers, is important for collecting accurate data for analysis.

o    Knowledge of data processing software, computer programming, and numerical analysis techniques is valuable for handling and analyzing biomechanical data sets.

6.    Experimental Design:

o    Understanding research methodologies, experimental design, and data collection protocols is essential for conducting biomechanical studies and ensuring the validity and reliability of results.

o    Knowledge of ethical considerations, participant recruitment, and data management practices is important for conducting quantitative analyses in a research setting.

By possessing a strong foundation in these prerequisite knowledge areas, researchers and practitioners can effectively perform quantitative analysis in biomechanics, interpret results accurately, and draw meaningful conclusions about human movement patterns, performance, and injury mechanisms.

 

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