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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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Nicholl - Lee Nicholl (NLN) Line Clipping in Computer Graphics

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 In the Cohen-Sutherland method, multiple intersections may be calculated along the path of a single line before an intersection on the clipping rectangle or line is completely rejected.  In Nicholl - Lee Nicholl Line Clipping, Extra intersection calculations are eliminated.  ADVANTAGE OF Nicholl - Lee Nicholl (NLN) Line Clipping 1.) The  number of clipping points is reduced to one, compared to other algorithms that may require two or more clipping.  2.)  Nicholl - Lee Nicholl (NLN) Line Clipping is FAST. Clipping algorithms for Circle, Curved boundary clipping regions are SLOWER because intersections calculations involve nonlinear curve equations.  3.)    Nicholl - Lee Nicholl (NLN) Line Clipping avoids multiple clipping of Line segments.  4.) As Compare to Cohen Sutherland and Liang-Barsy  Nicholl - Lee Nicholl Line Clipping performed fewer comparisons.    How Nicholl - Lee Nicholl Line Clipping works?  Nicholl...
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