# librerias
from numpy import mean
from numpy import std
from pandas import read_csv
from sklearn.model_selection import LeaveOneOut
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import cross_val_score
from sklearn.ensemble import RandomForestClassifier

# librerias
from pandas import read_csv
# cargar datos
url = 'https://raw.githubusercontent.com/jbrownlee/Datasets/master/housing.csv'
dataframe = read_csv(url, header=None)
# shape
print(dataframe.shape)

dataframe

dataframe.dtypes

dataframe.isnull().sum()

# separar en X y y
data= dataframe.values
X, y = data[:, :-1], data[:, -1]
print(X.shape, y.shape)

from sklearn.model_selection import cross_val_score
from sklearn.ensemble import RandomForestRegressor
# crear el modelo
model = RandomForestRegressor(random_state=42, n_estimators=10,max_depth=4)
scores = cross_val_score(model, X, y, cv=7, verbose=1,scoring='r2')

print("%0.2f de r2 promedio con una desviacion estandar de %0.2f" % (scores.mean(), scores.std()))