Difference between using train_test_split and cross_val_score in sklearn.cross_validation

Question

I have a matrix with 20 columns. The last column are 0/1 labels.

The link to the data is here.

I am trying to run random forest on the dataset, using cross valid

Answer 1

The answer is what @KCzar pointed. Just want to note the easiest way I found to randomize data(X and y with the same index shuffling) is as following:

p = np.random.permutation(len(X))
X, y = X[p], y[p]

source: Better way to shuffle two numpy arrays in unison