# In[188]:
print(score)
print(pla_parameters)
# ### Pocket
# In[205]:
X, y = classification_array[:, :3], classification_array[:, 4]
X = np.c_[np.ones(len(X)), np.array(X)]
# In[206]:
pla = Perceptron()
pla.warm_start = True
best_score = 0
for i in range(0, 7000):
pla = pla.fit(X, y)
score = pla.score(X, y)
if (best_score <= score or i == 0):
best_score = score
param = pla.coef_
# In[207]:
print(best_score)
print(param)
# ## Linear Regression
