How the tree should be trained on this input data?

Question

How the tree should be trained on this input data?

Remote exam in Machine learning, it is necessary to write an algorithm decision tree for the following dataset:

x = []
 for i in range(500):
 x.append([round(-2+50*random()), round(-2+50*random())])
 x = np.array(x)
 y = round(random()+ x[1, :] + x[2, :] )

As using these data, I train a tree? Is it possible that Y is something missing?

agustina_Larson asked April 19th 20 at 12:46

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1 answer

agustina_Larson answered on · Accepted Answer · 2020-04-19T10:48:25.963Z

The code error, I copied.

X_train = []
for i in range(5000):
X_train.append([round(-2 + 50 * rand()), round(-2 + 50 * rand())])
X_train = np.array(X_train)
Y_train = round(rand()) + X_train[:, 0] + X_train[:, 1]

X_test = []
for i in range(500):
X_test.append([round(-2 + 50 * rand()), round(-2 + 50 * rand())])
X_test = np.array(X_test)
Y_test = round(rand()) + X_test[:, 0] + X_test[:, 1]

I have obtained 500 random values and need to be classified according to randomname count classes. Without sclera it was very difficult, but I did the code and it works, the issue is closed.