TheAlgorithms/C-Sharp · error · InvalidOperationException

Model must be fitted before prediction.

Error message

Model must be fitted before prediction.

What it means

Predict uses the Slope and Intercept computed by Fit; before Fit succeeds those are meaningless defaults. LinearRegression tracks IsFitted and throws InvalidOperationException('Model must be fitted before prediction.') if Predict is called first.

Solutions

1. Call Fit(x, y) successfully before any Predict call.
2. Check the IsFitted property before predicting and handle the unfitted case in app code.
3. If Fit failed earlier, fix the underlying data issue (null/empty/mismatch/zero variance) so it completes.

Example fix

// before
var regression = new LinearRegression();
var y = regression.Predict(2.5); // not fitted
// after
var regression = new LinearRegression();
regression.Fit(xs, ys);
if (!regression.IsFitted) throw new InvalidOperationException("Fit the model first.");
var y = regression.Predict(2.5);

if (!regression.IsFitted)
    throw new InvalidOperationException("Call Fit before Predict.");
var y = regression.Predict(x);

try
{
    var y = regression.Predict(x);
}
catch (InvalidOperationException)
{
    // model not fitted — run training first
    regression.Fit(xs, ys);
    var y = regression.Predict(x);
}

When it happens

Trigger: Calling Predict(x) on a new LinearRegression instance, or on one whose Fit call threw (null/empty/mismatched/zero-variance data), leaving IsFitted false.

Common situations: Using a model instance before the training pipeline ran; a silent Fit failure earlier in the flow; sharing a model across threads where Predict starts before Fit finishes.

Understand the failure class

Background: "Invalid state transition" errors: "status must be X, actually Y", "already rejected/charging/uninstalled", "cannot ... while running" — what they mean when a library rejects your call — this error's family across 31 libraries.

Related errors

• No training data available.
• Input data cannot be null.
• Input data cannot be empty.
• Input lists must have the same length.
• Variance of X must not be zero.

AI-assisted analysis of TheAlgorithms/C-Sharp@96e2905cab (2026-09-13). Data as JSON: /api/errors/0adbba5d0c5e552d. Report an issue: GitHub.

Appendix: source

Thrown at Algorithms/MachineLearning/LinearRegression.cs:79

            throw new ArgumentException("Variance of X must not be zero.");
        }

        Slope = numerator / denominator;
        Intercept = yMean - Slope * xMean;
        IsFitted = true;
    }

    /// <summary>
    /// Predicts the output value for a given input using the fitted model.
    /// </summary>
    /// <param name="x">Input value.</param>
    /// <returns>Predicted output value.</returns>
    /// <exception cref="InvalidOperationException">Thrown if the model is not fitted.</exception>
    public double Predict(double x)
    {
        if (!IsFitted)
        {
            throw new InvalidOperationException("Model must be fitted before prediction.");
        }

        return Intercept + Slope * x;
    }

    /// <summary>
    /// Predicts output values for a list of inputs using the fitted model.
    /// </summary>
    /// <param name="xValues">List of input values.</param>
    /// <returns>List of predicted output values.</returns>
    /// <exception cref="InvalidOperationException">Thrown if the model is not fitted.</exception>
    public IList<double> Predict(IList<double> xValues)
    {
        if (!IsFitted)
        {
            throw new InvalidOperationException("Model must be fitted before prediction.");
        }

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