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title: calculating accuracy from confusion matrix in python: a step-by-step tutorial
introduction:
accuracy is a crucial metric in evaluating the performance of a classification model. the confusion matrix provides a detailed breakdown of the model's predictions, making it a valuable tool for assessing performance. in this tutorial, we'll guide you through the process of calculating accuracy from a confusion matrix using python.
step 1: import necessary libraries
start by importing the required libraries in your python script or jupyter notebook:
step 2: create a sample confusion matrix
for the purpose of this tutorial, let's create a sample confusion matrix. replace this with your actual confusion matrix later.
step 3: calculate accuracy from the confusion matrix
now, calculate the accuracy using the confusion matrix:
step 4: understanding the results
the confusion matrix provides a breakdown of the model's performance. it is a 2x2 matrix with elements representing true positives (tp), true negatives (tn), false positives (fp), and false negatives (fn). here is a brief explanation:

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