Task: Train and Evaluate a LinearSVC to Beat a Baseline

Context

You are provided with a binary or multi-class classification dataset, split into distinct training and hidden test sets. An accuracy baseline (e.g., from a previous model or a heuristic) is also supplied. Your primary objective is to implement train() and test() functions that achieve the following:

• Train a LinearSVC model that surpasses the given baseline accuracy on the hidden test set.
• Employ sound validation techniques, such as cross-validation, strictly without any access or 'peeking' at the hidden test set during development or tuning.
• Experiment with both model-level and data-level adjustments. Document your attempts and identify which changes contributed to improved performance, with a particular emphasis on the effectiveness of data-side tweaks.

Assume that input features may consist of numeric data (which can be dense or sparse) and/or text data. Your implementation should include a flexible preprocessing pipeline or parameterization within your functions to appropriately handle these diverse input types.

Requirements

1. Implement a train() function that:
   • Accepts training data (X_train, y_train) and a baseline_accuracy.
   • Constructs a scikit-learn Pipeline incorporating necessary preprocessing steps and a LinearSVC classifier.
   • Tunes key hyperparameters for the LinearSVC via cross-validation. This function should report the cross-validation scores and the best-found parameters.
   • Returns the fitted pipeline and relevant training diagnostics (e.g., cross-validation accuracy).
2. Implement a test() function that:
   • Takes the previously fitted pipeline and the test data (X_test, y_test).
   • Returns the calculated test accuracy and a brief report confirming whether the baseline was successfully beaten.
3. Experiments and Reporting:
   • Explore model-level adjustments (e.g., C regularization parameter, class_weight handling, feature selection techniques).
   • Explore data-side adjustments (e.g., feature scaling, TF–IDF transformation for text, stop-word removal, data deduplication).
   • Provide a concise summary detailing which changes led to accuracy improvements. Specifically highlight instances where data-side tweaks proved particularly effective.

Constraints

• The classifier used must be sklearn.svm.LinearSVC.
• Accuracy is the sole evaluation metric.
• Strictly avoid data leakage: all preprocessing steps must be fitted exclusively on training folds.
• Cross-validation should be performed solely on the training data; the hidden test set is to be used only once, at the very end, for final evaluation.