In [1]:

#Carga de las librerías
import urllib.request
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.tree import DecisionTreeClassifier

In [2]:

#Descargamos los datos desde internet!
file_name = 'dataR2.csv'

def download_file(file_name):
    print('Descargando el dataset')
    url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/00451/dataR2.csv'
    urllib.request.urlretrieve(url, file_name)


download_file(file_name)

Descargando el dataset

In [3]:

#Ahora si cargamos los datos en jupyter!
data = pd.read_csv(file_name)
data.head()

Out[3]:

	Age	BMI	Glucose	Insulin	HOMA	Leptin	Adiponectin	Resistin	MCP.1	Classification
0	48	23.500000	70	2.707	0.467409	8.8071	9.702400	7.99585	417.114	1
1	83	20.690495	92	3.115	0.706897	8.8438	5.429285	4.06405	468.786	1
2	82	23.124670	91	4.498	1.009651	17.9393	22.432040	9.27715	554.697	1
3	68	21.367521	77	3.226	0.612725	9.8827	7.169560	12.76600	928.220	1
4	86	21.111111	92	3.549	0.805386	6.6994	4.819240	10.57635	773.920	1

Insights:

El dataset se compone de 10 columnas de las cuales 9 corresponden a variables independientes, que usaremos para predecir el target.
Classification es la variable a predecir. Todas las variables son numéricas, ya sea enteras o reales y no tiene valores nulos.

In [4]:

#Separamos en X e y
X = data.drop(["Classification"], axis=1)
y = data["Classification"]

In [5]:

#Separamos en train y test!
(X_train, X_test,y_train, y_test) = train_test_split(X,y,stratify=y,test_size=0.30,random_state=42)

In [6]:

#Creamos un arbol de decisión sencillo y lo fiteamos
tree = DecisionTreeClassifier(random_state=42)
tree.fit(X_train, y_train)

Out[6]:

DecisionTreeClassifier(random_state=42)

In [7]:

y_test_pred = tree.predict(X_test) #Prediccion en Test

A lo largo de este notebook, se solicita calcular las métricas requeridas como así también su correspondiente interpretación:

Calcular la métrica Accuracy.

In [8]:

###Completar
from sklearn.metrics import accuracy_score
accuracy_score(y_test,y_test_pred)

Out[8]:

0.7714285714285715

Crear la Matriz de Confusión

In [9]:

###Completar
from sklearn.metrics import confusion_matrix
confusion_matrix(y_test, y_test_pred) 

Out[9]:

array([[13,  3],
       [ 5, 14]])

Calcular la métrica Recall

In [10]:

###Completar
from sklearn.metrics import precision_score
precision_score(y_test, y_test_pred) 

Out[10]:

0.7222222222222222

4.Calcular la métrica F1 score

In [11]:

###Completar
from sklearn.metrics import recall_score
recall_score(y_test, y_test_pred) 

Out[11]:

0.8125

In [12]:

from sklearn.metrics import f1_score
f1_score(y_test, y_test_pred) 

Out[12]:

0.7647058823529411

Random Forest¶

In [13]:

#Creamos un random forest!
model = RandomForestClassifier(random_state=42, n_estimators=100,
                               class_weight="balanced", max_features="log2")
model.fit(X_train, y_train)

Out[13]:

RandomForestClassifier(class_weight='balanced', max_features='log2',
                       random_state=42)

In [14]:

y_test_pred = model.predict(X_test) #Prediccion en Test

Calcular la métrica Accuracy.

In [15]:

###Completar
from sklearn.metrics import accuracy_score
accuracy_score(y_test,y_test_pred)

Out[15]:

0.7714285714285715

Calcular la métrica Precision

In [16]:

###Completar
from sklearn.metrics import precision_score
precision_score(y_test, y_test_pred) 

Out[16]:

0.7

Calcular la métrica Recall

In [17]:

###Completar
from sklearn.metrics import recall_score
recall_score(y_test, y_test_pred) 

Out[17]:

0.875

Calcular la métrica F1 score

In [18]:

###Completar
from sklearn.metrics import f1_score
f1_score(y_test, y_test_pred) 

Out[18]:

0.7777777777777777

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