I had not written for a while (the situation in Venezuela is not easy).

I will gradually pick the blog articles back up; I have some things on Artificial Intelligence (Neural Networks, Fuzzy Logic, machine learning and deep learning) and design patterns with Python.

This article is about a prediction model called a decision tree. According to wikipedia (es): It is a prediction model used in various fields ranging from artificial intelligence to economics. Given a set of data, diagrams of logical constructions are built, very similar to rule-based prediction systems, which serve to represent and categorize a series of conditions that occur successively, in order to solve a problem.

On youtube there is a channel dedicated to data science, mainly with Python; the youtuber is Siraj Raval. In the following video he explains how to make a gender classifier built with a Decision tree; you can watch the video at the following link.

The source code of the exercise in the video can be seen on github.

The idea is to have the height, weight and shoe size data and whether the person is a man or a woman; the decision tree is built, the data is passed to it, and from there, when another data point is passed, it produces an output saying whether it is a man or a woman.

Below is the code:

#!/usr/bin/env python

"""

https://www.youtube.com/watch?v=T5pRlIbr6gg

https://github.com/llSourcell/gender_classification_challenge/blob/master/demo.py



"""

#Se importa la librería sklearn el módulo tre

from sklearn import tree



#Se crea la instancia del árbol de decisión.

clf = tree.DecisionTreeClassifier()



#[altura, peso, talla de zapato]

X = [[181, 80, 44], [177, 70, 43], [160, 60, 38], [154, 54, 37], [166, 65, 40],

     [190, 90, 47], [175, 64, 39],

     [177, 70, 40], [159, 55, 37], [171, 75, 42], [181, 85, 43]]



#La salida donde se dice si es hombre o mujer

Y = ['hombre', 'hombre', 'mujer', 'mujer', 'hombre', 'hombre', 'mujer', 'mujer',

     'mujer', 'hombre', 'hombre']



#Se le pasa los datos  X y Y

clf = clf.fit(X, Y)



#Se definen los datos 1 y 2

dato1 = [190, 70, 43]

dato2 = [185, 62, 37]

prediction = clf.predict([dato1])

#Se muestra el resultado de la predicción de dato1

print(prediction)



prediction = clf.predict([dato2])


#Se muestra el resultado de la predicción de dato 2
print(prediction)

Running the script gives:

python3 arboldecision.py 
['hombre']
['mujer']

It means that, for a height of 1.90 m, a weight of 70 kilograms and a size of 43, the result is that it is a man, and for a height of 1.85 m, a weight of 62 kilograms and a size of 37, the result is that it is a woman.

I recommend watching the whole video to get a better explanation of how the decision tree works.

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