TheAlgorithms/Python · error · ValueError

Input data set must be one-dimensional

Error message

Input data set must be one-dimensional

What it means

Raised by DecisionTree.train when the x argument is not a one-dimensional numpy array. This regression tree splits on a single scalar feature, so a 2D x (multiple features or a column vector shaped (n, 1)) is rejected up front.

Source

Thrown at machine_learning/decision_tree.py:84

        3. Try to train when x and y are not of the same length
        >>> dt = DecisionTree()
        >>> dt.train(np.array([1,2,3,4,5]),np.array([[0,0,0,1,1],[0,0,0,1,1]]))
        Traceback (most recent call last):
            ...
        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.

View on GitHub (pinned to f5988cc097)

Solutions

1. Flatten x to 1D: x = np.asarray(x).ravel() or use df['col'].values.
2. Select a single feature column explicitly before training.
3. For multivariate features you need a different model; this class only supports one scalar input dimension.

Example fix

# before
dt.train(df[['height']].values, y)

# after
dt.train(df['height'].values.ravel(), y)

x = np.asarray(x)
if x.ndim != 1:
    x = x.ravel()
dt.train(x, y)

def is_1d_array(a) -> bool:
    return isinstance(a, np.ndarray) and a.ndim == 1

try:
    dt.train(x, y)
except ValueError as e:
    if "one-dimensional" in str(e) and x.ndim != 1:
        dt.train(np.asarray(x).ravel(), y)
    else:
        raise

When it happens

Trigger: Calling dt.train(np.array([[1], [2], [3]]), y) with a column-vector x, or passing a 2D feature matrix with several columns as produced by most sklearn-style pipelines.

Common situations: Loading data with pandas and passing df[['feature']].values (shape (n, 1)) instead of df['feature'].values (shape (n,)), or adapting code from scikit-learn which expects 2D X.

Related errors

• Data set labels 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/1b9f034ef6a7d0b4. Report an issue: GitHub.