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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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Objective Function Evaluation | Greedy Method | Knapsack Problem Example...

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Knapsack Problem using Greedy Method


Algorithm Design Techniques
  • Divide and Conquer
  • Greedy Method
  • Dynamic Programming
  • Back Tracing
  • Branch and Bound
Divide and Conquer: Many algorithms are recursive in structure. To solve any problem, they call themselves recursively again and again [one or more times]. Three steps are followed by divide and conquer algorithms.

1.) Divide the problem into the number of sub-problems.
2.) Conquer the sub-problems by solving them recursively.
3.) Combine the solution to the sub-problems into the solution for the original problem.

The greedy method is the Straight design technique. It can be applied to a wide variety of problems. Obtain a subset that satisfies the same constraints.  Feasible Solution: If any subset satisfies these constraints. 
Our GOAL: Find a feasible solution that either Maximize or Minimize the given Objective Function. A feasible solution that does this is known as OPTIMAL SOLUTION.  A feasible Solution is any subset that satisfies these constraints.

Greedy Method Example : KNAPSACK PROBLEM
SUPPOSE: We have 
        n  = Objects and a Knapsack.
𝑤_𝑖 = Object i has weight 
 m = Knapsack Capacity

IF a fraction 𝑥_𝑖, of object i is placed into the knapsack. 0 ≤ 𝑥_𝑖 ≤ 1 than Profit Earned.
Objective: Obtain filling of Knapsack and Gain maximum profit.


n = 3;                         //Objects
m = 20;                                 //Knapsack Capacity
𝑤1,𝑤2,𝑤3 = 18, 15,10; //Objects Weight
𝑃1,𝑃2,𝑃3 = 25, 24, 15; //Profits

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