Random forest prediction values

Question

Having a dataset like this:

         y     x    size    type    total_neighbours    res
113040  29  1204      15       3                   2      0
66281   52   402       9       3                   3      0
32296   21  1377      35       0                   3      0
48367    3   379     139       0                   4      0
33501    1    66      17       0                   3      0
... ... ... ... ... ... ...
131230  39  1002     439       3                   4      6
131237  40  1301      70       1                   2      1
131673  26  1124     365       1                   2      1
131678  27  1002     629       3                   3      6
131684  28  1301      67       1                   2      1

I would like to use random forest algorithm to predict the value of res column (res column can only take integer values between [0-6])

I'm doing it like this:

labels = np.array(features['res'])
features= features.drop('res', axis = 1)
features = np.array(features)

train_features, test_features, train_labels, test_labels = train_test_split(features, labels, test_size = 0.25,
                                                                           random_state = 42)

rf = RandomForestRegressor(n_estimators= 1000, random_state=42)

rf.fit(train_features, train_labels);
predictions = rf.predict(test_features)

The prediction I get are the following:

array([1.045e+00, 4.824e+00, 4.608e+00, 1.200e-01, 5.982e+00, 3.660e-01,
       4.659e+00, 5.239e+00, 5.982e+00, 1.524e+00])

I have no experience on this field so I don't quite understand the predictions.

1. How do I interpret them?
2. Is there any way to limit the predictions to the res column values ( integers between [0-6] )?

Thanks

Answer 1

As @MaxNoe said, I had a misconception about the model. I was using a regression to predict a discrete variable .

RandomForestClassifier is giving the expected output.