from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
import numpy as np
import pandas as pd

Obteniendo los datos

!wget https://raw.githubusercontent.com/susanli2016/Machine-Learning-with-Python/master/diabetes.csv

--2018-10-14 23:42:51--  https://raw.githubusercontent.com/susanli2016/Machine-Learning-with-Python/master/diabetes.csv
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 23875 (23K) [text/plain]
Saving to: ‘diabetes.csv’

diabetes.csv        100%[===================>]  23.32K  --.-KB/s    in 0.008s  

2018-10-14 23:42:51 (2.91 MB/s) - ‘diabetes.csv’ saved [23875/23875]

dataset = pd.read_csv("diabetes.csv")

dataset.head()

	Pregnancies	Glucose	BloodPressure	SkinThickness	Insulin	BMI	DiabetesPedigreeFunction	Age	Outcome
0	6	148	72	35	0	33.6	0.627	50	1
1	1	85	66	29	0	26.6	0.351	31	0
2	8	183	64	0	0	23.3	0.672	32	1
3	1	89	66	23	94	28.1	0.167	21	0
4	0	137	40	35	168	43.1	2.288	33	1

dataset.shape

(768, 9)

features = dataset.drop(["Outcome"], axis=1)
X = np.array(features)
y = np.array(dataset["Outcome"])

X_train, X_val, y_train, y_val = train_test_split(X, y, random_state=0, test_size=0.20)

Creando el modelo

tree = DecisionTreeClassifier()

tree.fit(X_train, y_train)

DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
            max_features=None, max_leaf_nodes=None,
            min_impurity_decrease=0.0, min_impurity_split=None,
            min_samples_leaf=1, min_samples_split=2,
            min_weight_fraction_leaf=0.0, presort=False, random_state=None,
            splitter='best')

tree.tree_.max_depth

15

validation_prediction = tree.predict(X_val)
training_prediction = tree.predict(X_train)

print('Exactitud training data: ', accuracy_score(y_true=y_train, y_pred=training_prediction))
print('Exactitud validation data: ', accuracy_score(y_true=y_val, y_pred=validation_prediction))

Exactitud training data:  1.0
Exactitud validation data:  0.7922077922077922

Mostrando el arbol de forma visual

!apt-get install graphviz

!pip install graphviz

Collecting graphviz
  Downloading https://files.pythonhosted.org/packages/47/87/313cd4ea4f75472826acb74c57f94fc83e04ba93e4ccf35656f6b7f502e2/graphviz-0.9-py2.py3-none-any.whl
Installing collected packages: graphviz
Successfully installed graphviz-0.9

import graphviz 
from sklearn.tree import export_graphviz

feature_names = features.columns

dot_data = export_graphviz(tree, out_file=None, 
                         feature_names=feature_names,  
                         class_names=True,  
                         filled=True, rounded=True,  
                         special_characters=True)  
graph = graphviz.Source(dot_data)

graph

Creando el segundo modelo

tree = DecisionTreeClassifier(min_samples_leaf=10, max_depth=8, min_samples_split=50)

tree.fit(X_train, y_train)

DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=8,
            max_features=None, max_leaf_nodes=None,
            min_impurity_decrease=0.0, min_impurity_split=None,
            min_samples_leaf=10, min_samples_split=50,
            min_weight_fraction_leaf=0.0, presort=False, random_state=None,
            splitter='best')

validation_prediction = tree.predict(X_val)
training_prediction = tree.predict(X_train)

print('Exactitud training data: ', accuracy_score(y_true=y_train, y_pred=training_prediction))
print('Exactitud validation data: ', accuracy_score(y_true=y_val, y_pred=validation_prediction))

Exactitud training data:  0.7964169381107492
Exactitud validation data:  0.8116883116883117

dot_data = export_graphviz(tree, out_file=None, 
                         feature_names=feature_names,  
                         class_names=True,  
                         filled=True, rounded=True,  
                         special_characters=True)  
graph = graphviz.Source(dot_data)

graph