learn-python/contrib/machine-learning/confusion-matrix.md

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