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contrib/machine-learning/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.
+
+For binary classification, it results in a 2 x 2 matrix that outlines four key parameters:
+1. True Positive (TP) - The predicted value matches the actual value, or the predicted class matches the actual class. 
+For example - the actual value was positive, and the model predicted a positive value.
+2. True Negative (TN) - The predicted value matches the actual value, or the predicted class matches the actual class. 
+For example - the actual value was negative, and the model predicted a negative value.
+3. False Positive (FP)/Type I Error - The predicted value was falsely predicted.
+For example - the actual value was negative, but the model predicted a positive value.
+4. False Negative (FN)/Type II Error - The predicted value was falsely predicted.
+For example - the actual value was positive, but the model predicted a negative value.
+
+The confusion matrix enables the calculation of various metrics like accuracy, precision, recall, F1-Score and specificity.
+1. Accuracy - It represents the proportion of correctly classified instances out of the total number of instances in the dataset.
+2. Precision - It quantifies the accuracy of positive predictions made by the model.
+3. Recall -  It quantifies the ability of a model to correctly identify all positive instances in the dataset and is also known as sensitivity or true positive rate.
+4. F1-Score - It is a single measure that combines precision and recall, offering a balanced evaluation of a classification model's effectiveness.
+
+To implement the confusion matrix in Python, we can use the confusion_matrix() function from the sklearn.metrics module of the scikit-learn library. 
+The function returns a 2D array that represents the confusion matrix.
+We can also visualize the confusion matrix using a heatmap.
+
+# Import necessary libraries
+import numpy as np
+from sklearn.metrics import confusion_matrix,classification_report
+import seaborn as sns
+import matplotlib.pyplot as plt 
+
+# Create the NumPy array for actual and predicted labels
+actual = np.array(['Apple', 'Apple', 'Apple', 'Not Apple', 'Apple', 'Not Apple', 'Apple', 'Apple', 'Not Apple', 'Not Apple'])
+predicted = np.array(['Apple', 'Not Apple', 'Apple', 'Not Apple', 'Apple', 'Apple', 'Apple', 'Apple', 'Not Apple', 'Not Apple'])
+
+# Compute the confusion matrix
+cm = confusion_matrix(actual,predicted)
+
+# Plot the confusion matrix with the help of the seaborn heatmap
+sns.heatmap(cm, 
+            annot=True,
+            fmt='g', 
+            xticklabels=['Apple', 'Not Apple'],
+            yticklabels=['Apple', 'Not Apple'])
+plt.xlabel('Prediction', fontsize=13)
+plt.ylabel('Actual', fontsize=13)
+plt.title('Confusion Matrix', fontsize=17)
+plt.show()
+
+# Classifications Report based on Confusion Metrics
+print(classification_report(actual, predicted))
+
+# Results 
+1. Confusion Matrix:
+[[5 1]
+[1 3]]
+2. Classification Report:
+              precision  recall   f1-score   support
+Apple           0.83      0.83      0.83         6
+Not Apple       0.75      0.75      0.75         4
+
+accuracy                            0.80        10
+macro avg       0.79      0.79      0.79        10
+weighted avg    0.80      0.80      0.80        10

contrib/machine-learning/index.md

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 # List of sections
 
-- [Section title](filename.md)
+- [Confusion Matrix](confusion-matrix.md)