Python Artificial Intelligence Projects for Beginners

binary classification
, which means classifying the objects of
a given set into two groups, but techniques do exist for multiclass classification as well. We
would require a large number of images of apples and oranges, and a machine learning
algorithm that would be set in such a way that the application would be able to classify 
both image types. In other words, we make these algorithms learn the difference between
the two objects to help classify all the examples correctly. This is known as 
supervised
learning
.
Now let's compare supervised learning with unsupervised learning. Let's assume that we
are not aware of the actual data labels (which means we do not know whether the images
are examples of apples or oranges). In such cases, classification won't be of much help. The
clustering
 method can always ease such scenarios. The result would be a model that can be
deployed in an application, and it would function as seen in the following diagram. The
application would memorize facts about the distinction between apples and oranges and
recognize actual images using a machine learning algorithm. If we took a new input, the
model would tell us about its decision as to whether the input is an apple or orange. In this
example, the application that we created is able to identify an image of an apple with a 75%
degree of confidence:

---

Building Your Own Prediction Models
Chapter 1
[ 7 ]
Sometimes, we want to know the level of confidence, and other times we just want the final
answer, that is, the choice in which the model has the most confidence.
Evaluation
We can evaluate how well the model is working by measuring its accuracy. Accuracy
would be defined as the percentage of cases that are classified correctly. We can analyze the
mistakes made by the model, or its level of confusion, using a confusion matrix. The
confusion matrix refers to the confusion in the model, but these confusion matrices can
become a little difficult to understand when they become very large. Let's take a look at the
following binary classification example, which shows the number of times that the model
has made the correct predictions of the object:
In the preceding table, the rows of