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- from sklearn.metrics import confusion_matrix
- from matplotlib import pyplot as plt
- # Plot confusion matrix
- def plot_confusion_matrix(cm, classes,
- normalize=False,
- title='Confusion matrix',
- cmap=plt.cm.Blues):
- """
- This function prints and plots the confusion matrix.
- Normalization can be applied by setting `normalize=True`.
- """
- plt.imshow(cm, interpolation='nearest', cmap=cmap)
- plt.title(title)
- plt.colorbar()
- tick_marks = np.arange(len(classes))
- plt.xticks(tick_marks, classes, rotation=45)
- plt.yticks(tick_marks, classes)
- if normalize:
- cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
- cm = np.round(cm,2)
- print("Normalized confusion matrix")
- else:
- print('Modelling: Confusion matrix, without normalization')
- thresh = cm.max() / 2.
- for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
- plt.text(j, i, cm[i, j],
- horizontalalignment="center",
- color="white" if cm[i, j] > thresh else "black")
- plt.tight_layout()
- plt.ylabel('True label')
- plt.xlabel('Predicted label')
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