Expand description
§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
§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.
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());
}§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.