#!/usr/bin/env python
# coding: utf-8

# Como no podía ser de otra manera, vamos a ver una implementación del algoritmo de Adaboost en nuestro famoso dataset de Iris :)

# In[1]:


from sklearn.ensemble import AdaBoostClassifier
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn import metrics


# In[2]:


from sklearn.ensemble import AdaBoostRegressor


# In[3]:


#Cargamos la data!
iris = datasets.load_iris()
X = iris.data
y = iris.target


# In[4]:


X


# In[5]:


y


# In[6]:


#Separación en Train y Test
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3) # 70% training and 30% test


# In[7]:


# Creamos un AdaBoostClassifier
ada = AdaBoostClassifier(n_estimators=50,learning_rate=0.1)


# In[8]:


#Fiteamos el modelo
model = ada.fit(X_train, y_train)


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#Predicción!
y_pred = model.predict(X_test)


# In[10]:


y_pred


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#Accuracy!
print("Accuracy:",metrics.accuracy_score(y_test, y_pred))


# In[12]:


from sklearn.metrics import classification_report
print(classification_report(y_test,y_pred))


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