Regresión Elastica

RegresionElastic.py

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+from sklearn.linear_model import ElasticNet
+from sklearn.preprocessing import PolynomialFeatures
+import numpy as np
+import matplotlib.pyplot as plt
+###############################
+#Datos originales
+###############################
+m = 100
+X = 6 * np.random.rand(m, 1) - 3
+y = 0.5 * X**2 + X + 2 + np.random.randn(m, 1)
+
+plt.plot(X,y,".", label = "Datos originales")
+###############################
+poly_features = PolynomialFeatures(degree=2, include_bias=False)
+X_pol = poly_features.fit_transform(X)
+elastic_net = ElasticNet(alpha=0.1, l1_ratio=0.5)
+elastic_net.fit(X_pol, y)
+yout=elastic_net.predict(X_pol)
+plt.plot(X,yout,"*", label = "Predicciones")
+
+
+# naming the x axis 
+plt.xlabel('Eje X') 
+# naming the y axis 
+plt.ylabel('Eje Y') 
+# giving a title to my graph 
+
+plt.legend() 
+plt.show()