# Examples
Runnable examples from the `examples/` directory. Each can be run with `cargo run --example <name>`.
## Basic preprocessing
`cargo run --example basic_preprocessing`
Standardize, scale, and encode a small mixed-type dataset. Demonstrates `StandardScaler`, `MinMaxScaler`, `OneHotEncoder`, and the `Transformer` trait.
## Pipeline workflow
`cargo run --example pipeline_workflow`
Build a 3-step pipeline (StandardScaler → MinMaxScaler → RobustScaler), fit and transform, then inspect individual steps via the runtime ergonomics API.
## Target encoding
`cargo run --example target_encoding`
Use `TargetEncoder` with smoothed mean encoding on a high-cardinality categorical feature, including the `TargetTransformer` trait and `fit_with_target`.
## Regression workflow
`cargo run --example regression_workflow`
End-to-end regression on synthetic house-price data: generate data, `train_test_split`, fit a `StandardScaler` **on train only** (data-leakage prevention), train a `Ridge`, score test R², then run 5-fold `cross_val_score`. Shows the full preprocess → train → evaluate → cross-validate loop on a single realistic dataset.
## Classification workflow
`cargo run --example classification_workflow`
Customer-churn binary classification with mixed numeric + categorical features. A `ColumnTransformer` applies `StandardScaler` to numeric columns and `OneHotEncoder` to the categorical column, feeding a `LogisticRegression`. Reports the full metric suite (`accuracy`, `precision`, `recall`, `f1`, `confusion_matrix`, `log_loss`), compares decision thresholds (0.3 / 0.5 / 0.7) to show the precision–recall trade-off, and finishes with **stratified** 5-fold cross-validation driven manually (`StratifiedKFold::split`, since `cross_val_score` only accepts `KFold`).
## Regularization comparison
`cargo run --example regularization_comparison`
Compares `Ridge` (L2) and `Lasso` (L1) across multiple `alpha` values on an 8-feature dataset where only 3 features carry signal, one feature is collinear, and the rest are pure noise. Ridge shrinks all coefficients but zeros none; Lasso's soft-thresholding drives the noise features to **exactly zero** (automatic feature selection). The collinear feature would make `LinearRegression` singular — both regularized solvers handle it.
## Model persistence
`cargo run --example model_persistence --features serde`
Production-style model persistence. Trains a `SupervisedPipeline<Ridge>` (StandardScaler → PCA → Ridge), writes it to a JSON file with `save_json`, reloads it with `load_json`, and confirms the restored model is still `is_fitted()` and produces bit-identical predictions **without refitting**. Requires the `serde` feature.
## KMeans clustering
`cargo run --example kmeans_clustering`
Unsupervised clustering on synthetic 2-D point data. Generates three well-separated blobs, fits a `KMeans` with k-means++ initialization, inspects the learned centroids against the true blob centers, predicts cluster assignments for new points, compares k-means++ vs random initialization by inertia, and (with `--features serde`) round-trips the fitted model through JSON. Demonstrates the `Clusterer` trait.
## Benchmark comparison
`cargo run --release --example bench_compare_rust`
The deterministic benchmark harness that produces the numbers on the [Performance](./performance.md) page. Uses a xorshift64 PRNG (seed 42) so Rust and Python generate identical data. Pass a repetition count as an argument:
```sh
cargo run --release --features matrixmultiply --example bench_compare_rust 15
```
## Using embedded datasets
The `datasets` feature provides classic toy datasets (Iris, Breast Cancer,
Wine, Diabetes) as `const` arrays — no file I/O needed. See the
[Datasets guide](./guide/datasets.md) for the full API.
```rust
use datarust::datasets::iris;
use datarust::linear_model::LogisticRegression;
use datarust::traits::Predictor;
let data = iris::load();
let x = data.features();
let y = data.targets().to_vec();
let mut model = LogisticRegression::new().with_max_iter(200);
model.fit(&x, &y)?;
println!("Iris accuracy: {:.1}%", model.score(&x, &y)? * 100.0);
```