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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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WFLO in Python || Optimal Placement of Wind Turbines using PSO in Python...

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Wind turbine optimal placement using particle swarm optimization Implementation in Python. Video Chapters: 00:00 Introduction 00:30 Key Points 03:17 Implementation 05:50 Flowchart 06:32 Code 23:47 Apply PSO 31:53 Output SOURCE CODE import numpy as np import math import random #Probability Distribution Function def PDF(U,k,c): return (k / c) * (U / c)**(k - 1) * math.exp(-((U / c)**k)) #Calculate Alpha def Cal_alpha(Z,Z_o): return 0.5 / math.log (Z/Z_o) #Calculate Full Wake Effect def Full_WE(u_o,a,alpha,X,R_1): return u_o*(1-(2*a/(1+alpha*(X/R_1)**2))) #Calculate Partial Wake Effect def Partial_WE(u_o,a,alpha,X,R_1,A_Partial,A_Total): return u_o * (1-(2*a/(1+alpha*(X/R_1)**2)))*(A_Partial-A_Total) #Calculate No Wake Effect def No_WE(u_o): return u_o #Calculate Power def Power(u,Ideal_Power): if u<3: return 0 elif 3<=u<=12: return Ideal_Power elif 12<=u<=25: return 518.4 else: return 0 #Calcu...
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