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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

Invasive Weed Optimization (IWO) Algorithm Step-by-Step with Numerical E...

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Invasive Weed Optimization (IWO) Algorithm with Example Invasive Weed Optimization The invasive weed optimization algorithm (IWO) is a population-based metaheuristic optimization method inspired by the behavior of weed colonies. Weeds are u nwanted plants (plant in the wrong place). Weeds can change their behavior according to the environment and gets fitter. Weeds plant can be easily found in: Parks, Fields, Garden, and Lawns Invasive Weed Optimization Algorithm Steps. 1.) Initialization Phase Initialize all important parameters. 2.) Initialize Population. The initial population is created by spreading the finite number of seeds randomly in the search space. 3.) Compute Fitness Values.  Every seed will grow into a flowering plant and produce seeds. [Reproduction].  Seed production is based on fitness values so compute: Individual Fitness Value Best Fitness Value Worst Fitness Value 4.) Random distribution of germinated seeds. Determine new positions of seeds in the search sp...
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