contingency table

Contingency table

A contingency table shows all the classfications that have been done with the real value. In a contingency table you can see exaclty how things were wrongly classified as what.

Example

Here is an example table in a result when classifing languages.

Language	German	French	Dutch	Italian	English
German	4030.	1.	2.	4.	13.
French	3.	3385.	2.	11.	7.
Dutch	20.	2.	1230.	4.	24.
Italian	0.	4.	2.	10682.	4.
English	10.	20.	13.	43.	16559.

So what you can read from this table is that 4030 German words were classified as German 4030 times but also 3 French, 20 Dutch and 10 Itlian words were classified as German.

1 German word was also classiefied as French. 2 German words were also classiefied as Dutch. 4 German words were also classiefied as Italian. 13 German words were also classiefied as English.

So that is how you read these tables.

Here was my code to create the Evaluating Classification models scores.

def precision(contingency_table, label):
    index = labels2ids[label]
    result = contingency_table.T[index]
    
    tp = result[index]
    
    mask = np.ones(len(result), bool)
    mask[index] = False
    
    fp = sum(result[mask])
    
    return tp / (tp + fp)


def recall(contingency_table, label):
    ct = contingency_table
    index = labels2ids[label]
    
    result = ct.T[index]
    
    tp = result[index]
    
    fn = sum(map(lambda x : x[True if i == index else False for i in range(len(ct))](len(ct))))))))), ct.T))
    
    return tp / (tp + fn)
    


def F_measure(contingency_table, label, Beta=1):
    P = precision(contingency_table, label)
    R = recall(contingency_table, label)
    return ((Beta**2 + 1) * P*R) / (Beta**2 * P + R)