Python Artificial Intelligence Projects for Beginners

setosa
 was correctly predicted 13 times out of all the
examples of setosa images from the dataset. On the other hand, 
versicolor
 was predicted
correctly on 10 occasions, and there were 6 occasions where versicolor was predicted as
virginica.
 Now let's normalize our confusion matrix and show the percentage of the cases
that predicted image corrected or incorrectly. In our example we saw that the setosa species
was predicted correctly throughout:

---

Building Your Own Prediction Models
Chapter 1
[ 9 ]
5QWTEGUEKMKVNGCTPFQEU
During evaluation of the confusion matrix, we also saw that the system got confused
between two species: versicolor and virginica. This also gives us the conclusion that the
system is not able to identify species of virginica all the time.
For further instances, we need to be more aware that we cannot have really high accuracy
since the system will be trained and tested on the same data. This will lead to memorizing
the training set and overfitting of the model. Therefore, we should try to split the data into
training and testing sets, first in either 90/10% or 80/20%. Then we should use the training
set for developing the model and the test set for performing and calculating the accuracy of
the confusion matrix.

---

Building Your Own Prediction Models
Chapter 1
[ 10 ]
We need to be careful not to choose a really good testing set or a really bad testing set to get
the accuracy. Hence to be sure we use a validation known as