TheAlgorithms/Python · error · ValueError

Unknown kernel: {kernel}

Error message

Unknown kernel: {kernel}

What it means

Raised by the SupportVectorMachine constructor when the kernel string is anything other than 'linear' or 'rbf'. The constructor maps the string to an internal kernel method and has no fallback, so an unrecognized name fails fast with a message echoing the bad value.

Source

Thrown at machine_learning/support_vector_machines.py:78

    ) -> None:
        self.regularization = regularization
        self.gamma = gamma
        if kernel == "linear":
            self.kernel = self.__linear
        elif kernel == "rbf":
            if self.gamma == 0:
                raise ValueError("rbf kernel requires gamma")
            if not isinstance(self.gamma, (float, int)):
                raise ValueError("gamma must be float or int")
            if not self.gamma > 0:
                raise ValueError("gamma must be > 0")
            self.kernel = self.__rbf
            # in the future, there could be a default value like in sklearn
            # sklear: def_gamma = 1/(n_features * X.var()) (wiki)
            # previously it was 1/(n_features)
        else:
            msg = f"Unknown kernel: {kernel}"
            raise ValueError(msg)

    # kernels
    def __linear(self, vector1: ndarray, vector2: ndarray) -> float:
        """Linear kernel (as if no kernel used at all)"""
        return np.dot(vector1, vector2)

    def __rbf(self, vector1: ndarray, vector2: ndarray) -> float:
        """
        RBF: Radial Basis Function Kernel

        Note: for more information see:
            https://en.wikipedia.org/wiki/Radial_basis_function_kernel

        Args:
            vector1 (ndarray): first vector
            vector2 (ndarray): second vector)

        Returns:

View on GitHub (pinned to f5988cc097)

Solutions

1. Use exactly 'linear' or 'rbf'.
2. If you imported the name from elsewhere, strip and lower-case it: kernel=kernel.strip().lower().
3. For kernels this class lacks (poly, sigmoid), implement them externally or use a library that supports them.

Example fix

# before
svm = SupportVectorMachine(kernel='poly')

# after
svm = SupportVectorMachine(kernel='rbf')  # or 'linear'; only these exist

kernel = kernel.strip().lower()
if kernel not in ('linear', 'rbf'):
    raise ValueError(f"unsupported kernel {kernel!r}; use 'linear' or 'rbf'")
svm = SupportVectorMachine(kernel=kernel)

def is_supported_kernel(name: str) -> bool:
    return isinstance(name, str) and name.strip().lower() in ('linear', 'rbf')

When it happens

Trigger: Calling SupportVectorMachine(kernel='poly'), kernel='sigmoid', or any typo like 'RBF' or 'Linear' (matching is case-sensitive).

Common situations: Copying kernel names valid in sklearn (poly, sigmoid, precomputed) into this class, case mismatches, or whitespace in config strings (' rbf').

Related errors

• gamma must be float or int
• Length of predicted and actual array must be same.
• y_true can have values -1 or 1 only.
• Test samples' feature length does not equal to that of train
• gamma value must be non-negative

AI-assisted analysis of TheAlgorithms/Python@f5988cc097 (2026-08-14). Data as JSON: /api/errors/f4b47997cf8bd25b. Report an issue: GitHub.