dotnet/machinelearning · error · ArgumentException

Training data and validation data schemas do not match. Colu

Error message

Training data and validation data schemas do not match. Column '{trainCol.Name}' is of type {trainCol.Type} in train data, and type {validCol.Value.Type} in validation data.

What it means

This ArgumentException is thrown when a column present in both train and validation data has different DataViewTypes (e.g. Single vs String, or different key/vector types). The message reports both types so the mismatched column is easy to spot.

Source

Thrown at src/Microsoft.ML.AutoML/Utils/UserInputValidationUtil.cs:222

            // Validate that every active column in the train data corresponds to an active column in the validation data.
            // (Indirectly, since we asserted above that the train and validation data have the same number of active columns, this also
            // ensures the reverse -- that every active column in the validation data corresponds to an active column in the train data.)
            foreach (var trainCol in trainData.Schema)
            {
                if (trainCol.IsHidden)
                {
                    continue;
                }

                var validCol = validationData.Schema.GetColumnOrNull(trainCol.Name);
                if (validCol == null)
                {
                    throw new ArgumentException($"{schemaMismatchError} Column '{trainCol.Name}' exists in train data, but not in validation data.", nameof(validationData));
                }

                if (trainCol.Type != validCol.Value.Type && !trainCol.Type.Equals(validCol.Value.Type))
                {
                    throw new ArgumentException($"{schemaMismatchError} Column '{trainCol.Name}' is of type {trainCol.Type} in train data, and type " +
                        $"{validCol.Value.Type} in validation data.", nameof(validationData));
                }
            }
        }

        private static void ValidateTrainDataColumns(IDataView trainData, IEnumerable<string> columnNames, string columnPurpose,
            IEnumerable<DataViewType> allowedTypes = null)
        {
            if (columnNames == null)
            {
                return;
            }

            foreach (var columnName in columnNames)
            {
                ValidateTrainDataColumn(trainData, columnName, columnPurpose, allowedTypes);
            }
        }

View on GitHub (pinned to 7b76e69cf9)

Solutions

  1. Use one explicit TextLoader.Options/schema for both train and validation loads so types agree.
  2. Convert validation column with ConvertTransform to the training column's type.
  3. Inspect and compare `trainData.Schema["col"].Type` vs validation before Execute.

Example fix

// before
var valData = mlContext.Data.LoadFromTextFile("val.csv", hasHeader: true); // inferred string for Amount
// after
var opts = new TextLoader.Options { Columns = new[]{ new TextLoader.Column("Amount", DataKind.Single, 3) } };
var valData = mlContext.Data.LoadFromTextFile("val.csv", opts);
Defensive patterns

Strategy: validation

Validate before calling

foreach (var col in trainData.Schema.Where(c => !c.IsHidden))
{
    var v = validationData.Schema.GetColumnOrNull(col.Name);
    if (v != null && col.Type != v.Value.Type && !col.Type.Equals(v.Value.Type))
        throw new InvalidOperationException($"type mismatch on {col.Name}: {col.Type} vs {v.Value.Type}");
}

Try / catch

try { result = experiment.Execute(trainData, validationData, ...); }
catch (ArgumentException ex) when (ex.Message.Contains("is of type")) { /* convert types and retry */ }

Prevention

When it happens

Trigger: Column typed as string in training file but numeric in validation file (or vice versa) because loaders inferred different types; KeyType/VectorType differing due to different loader options.

Common situations: Validation CSV has quoted numbers parsed as text; one file has missing values changing inferred type; TextLoader schema explicitly specified for one file but inferred for the other.

Understand the failure class

Background: Schema validation failed / invalid input schema: payload rejected because its shape doesn't match the expected schema — this error's family across 28 libraries.

Related errors

AI-assisted analysis of dotnet/machinelearning@7b76e69cf9 (2026-09-11). Data as JSON: /api/errors/4a8f20816bfaa693. Report an issue: GitHub.