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Update confusion-matrix.md
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Confusion Matrix - A confusion matrix is a fundamental performance evaluation tool used in machine learning to assess the accuracy of a classification model. It is an N x N matrix, where N represents the number of target classes.
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## Confusion Matrix
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A confusion matrix is a fundamental performance evaluation tool used in machine learning to assess the accuracy of a classification model. It is an N x N matrix, where N represents the number of target classes.
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For binary classification, it results in a 2 x 2 matrix that outlines four key parameters:
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1. True Positive (TP) - The predicted value matches the actual value, or the predicted class matches the actual class.
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The function returns a 2D array that represents the confusion matrix.
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We can also visualize the confusion matrix using a heatmap.
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```python
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# Import necessary libraries
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import numpy as np
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from sklearn.metrics import confusion_matrix,classification_report
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from sklearn.metrics import confusion_matrix, classification_report
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import seaborn as sns
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import matplotlib.pyplot as plt
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# Create the NumPy array for actual and predicted labels
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actual = np.array(['Apple', 'Apple', 'Apple', 'Not Apple', 'Apple', 'Not Apple', 'Apple', 'Apple', 'Not Apple', 'Not Apple'])
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predicted = np.array(['Apple', 'Not Apple', 'Apple', 'Not Apple', 'Apple', 'Apple', 'Apple', 'Apple', 'Not Apple', 'Not Apple'])
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actual = np.array(['Apple', 'Apple', 'Apple', 'Not Apple', 'Apple',
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'Not Apple', 'Apple', 'Apple', 'Not Apple', 'Not Apple'])
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predicted = np.array(['Apple', 'Not Apple', 'Apple', 'Not Apple', 'Apple',
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'Apple', 'Apple', 'Apple', 'Not Apple', 'Not Apple'])
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# Compute the confusion matrix
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cm = confusion_matrix(actual,predicted)
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# Classifications Report based on Confusion Metrics
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print(classification_report(actual, predicted))
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```
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# Results
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### Results
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```
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1. Confusion Matrix:
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[[5 1]
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[1 3]]
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accuracy 0.80 10
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macro avg 0.79 0.79 0.79 10
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weighted avg 0.80 0.80 0.80 10
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```
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