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Confusion Matrix with Real-Life Examples || Artificial Intelligence || ~...

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Learn about the Confusion Matrix with Real-Life Examples. A confusion matrix is a table that shows how well an AI model makes predictions. It compares the actual results with the predicted ones and tells which are right or wrong. It includes True Positive (TP), False Positive (FP), False Negative (FN), and True Negative (TN). Video Chapters: Confusion Matrix in Artificial Intelligence 00:00 Introduction 00:12 Confusion Matrix 03:48 Metrices Derived from Confusion Matrix 04:26 Confusion Matrix Example 1 05:44 Confusion Matrix Example 2 08:10 Confusion Matrix Real-Life Uses #artificialintelligence #machinelearning #confusionmatrix #algorithm #optimization #research #happylearning #algorithms #meta #optimizationtechniques #swarmintelligence #swarm #artificialintelligence #machinelearning
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Benchmarking Optimization Algorithms | Mean and Standard Deviation Calculation

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 Benchmarking Optimization Algorithms Watch Now:  https://youtu.be/uBlACmRLv14 Learn about Benchmark Functions & Role of Mean & Standard Deviation in Metaheuristics Video Chapters: Mean & SD Analysis in Optimization Algorithms 00:00 Introduction 00:33 Why Benchmarking is used in Metaheuristic Algorithms? 03:26 Benchmark Function Testing 07:53 Calculate Mean and SD from Benchmark Functions 12:12 Calculation using Python 12:30 Algorithms Comparison 13:40 Conclusion Benchmarking is essential in metaheuristic algorithms to evaluate and compare their performance using standardized test functions. It helps measure accuracy, stability, and efficiency before applying these algorithms to real-world problems. Key concepts include: Mean (μ): Indicates the average performance of an algorithm. Standard Deviation (σ): Measures result in variability across multiple runs, reflecting stability. Benchmark Functions: Artificial test functions (e.g., Sphere, Rastrigin, Ackl...
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