Real-time feature extraction from video stream data for stream ...

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Real-time feature extraction from video stream data for stream ...

3.2. Supervised Learning

Prediction: 0 Prediction: 1 ... Prediction 9 Total

Label: 0 94 3 ... 7 130

Label: 1 2 81 ... 2 98

... ... ... ... ... ...

Label: 9 3 0 ... 87 108

Total 122 98 ... 131 1000

Table 3.1.: Possible confusion matrix for the handwritten digit recognition problem.

As we can see, we had a total of 1000 examples. 130 were labeled as 0, 98 were labeled

as 1 et cetera. The inferred model predicted the label ˆ0 for 122 examples. 94 out of

that 122 were labeled correctly, the other 28 examples were labeled wrong. The same

goes for all other classes. Thus the accuracy of the model is given by summing up all

elements on the main diagonal (=correct classified examples) and dividing them by the

total number of examples. By the way, the error is then given by summing up all entries

of the confusion matrix except those on the main diagonal.

Definition 11 (Accuracy) Accuracy is the proportion of elements, which really belong

to the class they were predicted to belong to right predictions, based on the whole

population.

Accuracy =

|Correctly predicted items|

|All items|

(3.1)

In many real-world classification problems the number of classes is binary. Thus these

classification tasks are called binary classification tasks. Examples are the classification

of e-mail into spam and no spam, the classification of patients in those having a certain

disease or not, and the classification of news shots into anchorshots and news report

shots (see chapter 2.3.3).

In such a setting the resulting confusion matrix (see table 3.2) basically consists out of

four entries:

true positives (TP), counting all examples, that belong to the class we are looking

for and our model predicts this correctly,

true negatives (TN), holding all examples, that do not belong to the class we are

looking for and get labeled as such,

false negatives (FN), counting all items, that belong to the class we are looking but

our model claims they do not

false positives (FP) holding those items, that do not belong do the class we are

looking for but our model incorrectly claims them to do.

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