confusion matrix with labels sklearn
By definition, entry i,j in a confusion matrix is the number of
observations actually in group i, but predicted to be in group j.
Scikit-Learn provides a confusion_matrix function:
from sklearn.metrics import confusion_matrix
y_actu = [2, 0, 2, 2, 0, 1, 1, 2, 2, 0, 1, 2]
y_pred = [0, 0, 2, 1, 0, 2, 1, 0, 2, 0, 2, 2]
confusion_matrix(y_actu, y_pred)
# Output
# array([[3, 0, 0],
# [0, 1, 2],
# [2, 1, 3]], dtype=int64)
3.67
6
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 12
Thank you!
6
0
3.83
6
import numpy as np
currentDataClass = [1, 3, 3, 2, 5, 5, 3, 2, 1, 4, 3, 2, 1, 1, 2]
predictedClass = [1, 2, 3, 4, 2, 3, 3, 2, 1, 2, 3, 1, 5, 1, 1]
def comp_confmat(actual, predicted):
classes = np.unique(actual) # extract the different classes
matrix = np.zeros((len(classes), len(classes))) # initialize the confusion matrix with zeros
for i in range(len(classes)):
for j in range(len(classes)):
matrix[i, j] = np.sum((actual == classes[i]) & (predicted == classes[j]))
return matrix
comp_confmat(currentDataClass, predictedClass)
array([[3., 0., 0., 0., 1.],
[2., 1., 0., 1., 0.],
[0., 1., 3., 0., 0.],
[0., 1., 0., 0., 0.],
[0., 1., 1., 0., 0.]])
Thank you!
6
0
3.75
8
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)
Thank you!
8
0
0
0
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)
Thank you!
0
0
0
0
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)
Thank you!
0
0
0
0
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)
Thank you!
0
0
0
0
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)
Thank you!
0
0
0
0
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)
Thank you!
0
0
0
0
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)
Thank you!
0
0
0
0
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)
Thank you!
0
0
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