ValueError: Cannot have number of splits n_splits=

I am trying this training modeling using train_test_split and a decision tree regressor:

import sklearn
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
from sklearn.model_selection import cross_val_score

# TODO: Make a copy of the DataFrame, using the 'drop' function to drop the given feature
new_data = samples.drop('Fresh', 1)

# TODO: Split the data into training and testing sets using the given feature as the target
X_train, X_test, y_train, y_test = train_test_split(new_data, samples['Fresh'], test_size=0.25, random_state=0)

# TODO: Create a decision tree regressor and fit it to the training set
regressor = DecisionTreeRegressor(random_state=0)
regressor = regressor.fit(X_train, y_train)

# TODO: Report the score of the prediction using the testing set
score = cross_val_score(regressor, X_test, y_test, cv=3)

print score

When running this, I am getting the error:

ValueError: Cannot have number of splits n_splits=3 greater than the number of samples: 1.

If I change the value of cv to 1, I get:

ValueError: k-fold cross-validation requires at least one train/test split by setting n_splits=2 or more, got n_splits=1.

Some sample rows of the data look like:

    Fresh   Milk    Grocery Frozen  Detergents_Paper    Delicatessen
0   14755   899 1382    1765    56  749
1   1838    6380    2824    1218    1216    295
2   22096   3575    7041    11422   343 2564

If the number of splits is greater than number of samples, you will get the first error. Check the snippet from the source code given below:

if self.n_splits > n_samples:
    raise ValueError(
        ("Cannot have number of splits n_splits={0} greater"
         " than the number of samples: {1}.").format(self.n_splits,
                                                     n_samples))

If the number of folds is less than or equal 1, you will get the second error. In your case, the cv = 1. Check the source code:

if n_folds <= 1:
            raise ValueError(
                "k-fold cross validation requires at least one"
                " train / test split by setting n_folds=2 or more,"
                " got n_folds={0}.".format(n_folds))

An educated guess, the number of samples in X_test is less than 3. Check that carefully.