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We know that this problem is Multi-Class Classification Problem.
To get a confusion matrix for the same you can use the following command:
from mlxtend.evaluate import confusion_matrix
#import the required packages
from mlxtend.evaluate import confusion_matrix
from mlxtend.evaluate import plot_confusion_matrix
#Actual Target Values
y_target = [-1,1,0,1,-1,1,0,1,0,-1]
#Predicted Values
y_predicted = [-1,0,1,1,-1,0,1,1,0,-1]
#creation of confusion matrix
cm = confusion_matrix(y_target=y_target,
y_predicted=y_predicted,
binary=False)
#to print the calculated values of Confusion Matrix
cm
Outcome:
array([[3, 0, 0],
[0, 1, 2],
[0, 2, 2]])
For visualizing the cm you can use the following command:
fig, ax = plot_confusion_matrix(conf_mat=cm)
plt.show()
You can go through this Link for better understanding of mlextend.
You can get the Precision and Accuracy values by using the following formulas:
$\text{Precision}_{~i} = \cfrac{M_{ii}}{\sum_j M_{kji}}$
$\text{Recall}_{~i} = \cfrac{M_{ii}}{\sum_j M_{ijk}}$
Go through these Link-1,Link-2 for better understanding on how to compute the same, in the Link-3 is GitHub link which explains on how they implemented for a 1-D array, looking at that you can try expanding it for your outcome.
