print labels on confusion_matrix
unique_label = np.unique([y_true, y_pred])
cmtx = pd.DataFrame(
confusion_matrix(y_true, y_pred, labels=unique_label),
index=['true:{:}'.format(x) for x in unique_label],
columns=['pred:{:}'.format(x) for x in unique_label]
)
print(cmtx)
# Output:
# pred:no pred:yes
# true:no 3 0
# true:yes 2 1confusion matrix with labels sklearn
import pandas as pd
y_true = pd.Series([2, 0, 2, 2, 0, 1, 1, 2, 2, 0, 1, 2])
y_pred = pd.Series([0, 0, 2, 1, 0, 2, 1, 0, 2, 0, 2, 2])
pd.crosstab(y_true, y_pred, rownames=['True'], colnames=['Predicted'], margins=True)print labels on confusion_matrix
import pandas as pd
cmtx = pd.DataFrame(
confusion_matrix(y_true, y_pred, labels=['yes', 'no']),
index=['true:yes', 'true:no'],
columns=['pred:yes', 'pred:no']
)
print(cmtx)
# Output:
# pred:yes pred:no
# true:yes 1 2
# true:no 0 3confusion matrix with labels sklearn
Predicted 0 1 2 All
True
0 3 0 0 3
1 0 1 2 3
2 2 1 3 6
All 5 2 5 12Copyright © 2021 Codeinu
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