# Dendritic Trees Crate

 This crate contains all tree based machine learning models.
 Contains standard decision tree and random forest classifier and regressors.

 ## Features
 - **Decision Tree**: Standard scalar and min max normlization of data.
 - **Random Forest**: One hot encoding for multi class data
 - **Bootstrap**: One hot encoding for multi class data

 ## Disclaimer
 The dendritic project is a toy machine learning library built for learning and research purposes.
 It is not advised by the maintainer to use this library as a production ready machine learning library.
 This is a project that is still very much a work in progress.

 ## Example Usage
 This is an example of using the decision tree classifier model provided by dendritic.
 The example below uses decision trees but random forest can be used with  `RandomForestClassifier` or `RandomForestRegressor`.
 ```rust
 use dendritic_ndarray::ndarray::NDArray;
 use dendritic_ndarray::ops::*;
 use dendritic_metrics::loss::*;
 use dendritic_metrics::utils::*;
 use dendritic_trees::decision_tree::*;
 use dendritic_datasets::iris::*;
 
 fn main() {

    // load data
    let data_path = "../dendritic-datasets/data/iris.parquet";
    let max_depth = 3;
    let samples_split = 3; 
    let (x_train_test, y_train_test) = load_iris(data_path).unwrap();
    let (x_train, y_train) = load_all_iris(data_path).unwrap();
    
    // Decision tree classifier model
    let mut model = DecisionTreeClassifier::new(
        max_depth, 
        samples_split, 
        gini_impurity
    );
    model.fit(&x_train, &y_train);

    let sample_index = 100;
    let x_test = x_train_test.batch(5).unwrap();
    let y_test = y_train_test.batch(5).unwrap();
    let y_pred = model.predict(x_test[sample_index].clone());
    println!("Actual: {:?}", y_test[sample_index]);
    println!("Prediction: {:?}", y_pred.values()); 

 }
 ```