kopia lustrzana https://github.com/animator/learn-python
62 wiersze
3.3 KiB
Markdown
62 wiersze
3.3 KiB
Markdown
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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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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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For example - the actual value was positive, and the model predicted a positive value.
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2. True Negative (TN) - The predicted value matches the actual value, or the predicted class matches the actual class.
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For example - the actual value was negative, and the model predicted a negative value.
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3. False Positive (FP)/Type I Error - The predicted value was falsely predicted.
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For example - the actual value was negative, but the model predicted a positive value.
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4. False Negative (FN)/Type II Error - The predicted value was falsely predicted.
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For example - the actual value was positive, but the model predicted a negative value.
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The confusion matrix enables the calculation of various metrics like accuracy, precision, recall, F1-Score and specificity.
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1. Accuracy - It represents the proportion of correctly classified instances out of the total number of instances in the dataset.
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2. Precision - It quantifies the accuracy of positive predictions made by the model.
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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.
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4. F1-Score - It is a single measure that combines precision and recall, offering a balanced evaluation of a classification model's effectiveness.
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To implement the confusion matrix in Python, we can use the confusion_matrix() function from the sklearn.metrics module of the scikit-learn library.
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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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# 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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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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# Compute the confusion matrix
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cm = confusion_matrix(actual,predicted)
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# Plot the confusion matrix with the help of the seaborn heatmap
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sns.heatmap(cm,
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annot=True,
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fmt='g',
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xticklabels=['Apple', 'Not Apple'],
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yticklabels=['Apple', 'Not Apple'])
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plt.xlabel('Prediction', fontsize=13)
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plt.ylabel('Actual', fontsize=13)
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plt.title('Confusion Matrix', fontsize=17)
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plt.show()
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# Classifications Report based on Confusion Metrics
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print(classification_report(actual, predicted))
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# Results
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1. Confusion Matrix:
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[[5 1]
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[1 3]]
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2. Classification Report:
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precision recall f1-score support
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Apple 0.83 0.83 0.83 6
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Not Apple 0.75 0.75 0.75 4
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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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