TheAlgorithms/Python · error · ValueError

Data set labels must be one-dimensional

Error message

Data set labels must be one-dimensional

What it means

Raised by DecisionTree.train when the y (labels) argument is not one-dimensional, even if lengths match. The tree computes np.mean(y) for leaf predictions and compares scalar labels against the decision boundary, which only works for shape (n,) arrays.

Source

Thrown at machine_learning/decision_tree.py:88

            ...
        ValueError: x and y have different lengths

        4. Try to train when x & y are of the same length but different dimensions
        >>> dt = DecisionTree()
        >>> dt.train(np.array([1,2,3,4,5]),np.array([[1],[2],[3],[4],[5]]))
        Traceback (most recent call last):
            ...
        ValueError: Data set labels must be one-dimensional

        This section is to check that the inputs conform to our dimensionality
        constraints
        """
        if x.ndim != 1:
            raise ValueError("Input data set must be one-dimensional")
        if len(x) != len(y):
            raise ValueError("x and y have different lengths")
        if y.ndim != 1:
            raise ValueError("Data set labels must be one-dimensional")

        if len(x) < 2 * self.min_leaf_size:
            self.prediction = np.mean(y)
            return

        if self.depth == 1:
            self.prediction = np.mean(y)
            return

        best_split = 0
        min_error = self.mean_squared_error(x, np.mean(y)) * 2

        """
        loop over all possible splits for the decision tree. find the best split.
        if no split exists that is less than 2 * error for the entire array
        then the data set is not split and the average for the entire array is used as
        the predictor
        """

View on GitHub (pinned to f5988cc097)

Solutions

1. Flatten y: y = np.asarray(y).ravel().
2. Extract labels as a 1D Series: df['label'].values, not df[['label']].values.
3. Add a shared shape check (x.ndim == 1 and y.ndim == 1) in your data-prep function.

Example fix

# before
dt.train(x, y.reshape(-1, 1))

# after
dt.train(x, y.ravel())

x = np.asarray(x).ravel()
y = np.asarray(y).ravel()
dt.train(x, y)

def is_1d_pair(x, y) -> bool:
    return np.asarray(x).ndim == 1 and np.asarray(y).ndim == 1

try:
    dt.train(x, y)
except ValueError as e:
    if "one-dimensional" in str(e):
        dt.train(np.asarray(x).ravel(), np.asarray(y).ravel())
    else:
        raise

When it happens

Trigger: Calling dt.train(np.array([1,2,3,4,5]), np.array([[1],[2],[3],[4],[5]])) exactly as in the doctest — a column-vector y with shape (n, 1) has len equal to x but ndim == 2.

Common situations: Labels reshaped to (n, 1) for a keras/sklearn pipeline and reused here, or labels loaded via df[['label']].values instead of df['label'].values.

Related errors

• Input data set must be one-dimensional
• Shape of y_true and y_pred must be the same.
• Data must have dimensions N x 1
• Expected the same number of rows for A and B. Instead found
• Expected the same number of columns for B and C. Instead fou

AI-assisted analysis of TheAlgorithms/Python@f5988cc097 (2026-08-14). Data as JSON: /api/errors/e25fd69f8868c869. Report an issue: GitHub.