Task: Train and Evaluate a LinearSVC to Beat a Baseline

Context

You are given a binary or multi-class classification dataset split into train and hidden test sets. An accuracy baseline (e.g., a previous model or a heuristic) is provided. Your goal is to implement train() and test() functions to:

• Train a LinearSVC model that beats the supplied baseline on the hidden test set.
• Use sound validation (e.g., cross-validation) without peeking at the hidden test.
• Experiment with both model-level and data-level adjustments; document what you tried and which changes helped. Emphasize data-side tweaks where effective.

Assume input features may be numeric (dense or sparse) and/or text. You may implement a flexible preprocessing pipeline or parameterize your functions to handle either case.

Requirements

1. Implement train() that:
   • Accepts training data (X_train, y_train) and a baseline_accuracy .
   • Builds a scikit-learn Pipeline with preprocessing and a LinearSVC classifier.
   • Tunes key hyperparameters via cross-validation (report CV scores and best params).
   • Returns a fitted pipeline and training diagnostics (e.g., CV accuracy).
2. Implement test() that:
   • Takes the fitted pipeline and (X_test, y_test) .
   • Returns test accuracy (and a brief report) to confirm you beat the baseline.
3. Experiments and reporting:
   • Try model-level tweaks (e.g., C , class_weight , feature selection) and data-side tweaks (e.g., scaling, TF–IDF, stop-word removal, deduplication).
   • Briefly summarize which changes improved accuracy. Note that data-side tweaks were especially effective.

Constraints

• Classifier must be sklearn.svm.LinearSVC .
• Use accuracy as the metric.
• Avoid data leakage: fit all preprocessing only on training folds.
• Use cross-validation on train; use the hidden test only once at the end.