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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

Metaheuristics Search Space Design and Standard Testing Functions ~xRay ...

Metaheuristics Search Space Design and Standard Testing Functions


Video Chapters: Introduction: 00:00 Metaheuristics Testing Functions: 01:05 Metaheuristics Search Space: 05:40 Conclusion: 10:00

Metaheuristics Standard Benchmark Functions for Testing - Unimodel Test Functions - Multimodel Test Functions - Fixed Dimensions Test Functions - Hybrid Test Functions - Composition Test Functions Metaheuristics Search Space Design - Search Space - Search Space Bounds

Metaheuristic Search Space Design and Meta-heuristic testing functions:
Different testing functions that we can use for the comparison between different metaheuristic algorithms and we can analyze algorithm Performance, Stability, Convergence Speed, Accuracy, Efficiency and other.

Unimodal test functions are used to check algorithm convergence property and Exploitation capability and the fitness curve in the unimodal test function is used to check the algorithm convergence speed.


Multimodal test functions are used to check algorithm global search and local search.

Fixed dimension test functions to verify algorithm performance. We can verify accuracy, speed, and algorithm stability. In the fixed dimension test function, the dimension is fixed and you can see we have different types of fixed dimension test functions.


Search Space: search space in the metaheuristics plays a major role. Metaheuristic search is carried over the whole search space and search space is also known as Decision space State space or Search area or you can say Configuration space. Search space size represents the number of possibilities for the solution and in the search space boundary, Search space boundary we have a lower bound and upper bound.


The fitness function guides the whole search process toward the global optimal solution. In the search space for example you can see here suppose this is the search space and here we have 10 random solutions in search space you can see here:













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