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externcrate xgboost;use xgboost::{parameters,DMatrix,Booster};fnmain(){// training matrix with 5 training examples and 3 featureslet x_train =&[1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0];let num_rows =5;let y_train =&[1.0,1.0,1.0,0.0,1.0];// convert training data into XGBoost's matrix formatletmut dtrain =DMatrix::from_dense(x_train, num_rows).unwrap();// set ground truth labels for the training matrix    dtrain.set_labels(y_train).unwrap();// test matrix with 1 rowlet x_test =&[0.7,0.9,0.6];let num_rows =1;let y_test =&[1.0];letmut dtest =DMatrix::from_dense(x_test, num_rows).unwrap();    dtest.set_labels(y_test).unwrap();// configure objectives, metrics, etc.let learning_params = parameters::learning::LearningTaskParametersBuilder::default().objective(parameters::learning::Objective::BinaryLogistic).build().unwrap();// configure the tree-based learning model's parameterslet tree_params = parameters::tree::TreeBoosterParametersBuilder::default().max_depth(2).eta(1.0).build().unwrap();// overall configuration for Boosterlet booster_params = parameters::BoosterParametersBuilder::default().booster_type(parameters::BoosterType::Tree(tree_params)).learning_params(learning_params).verbose(true).build().unwrap();// specify datasets to evaluate against during traininglet evaluation_sets =&[(&dtrain,"train"),(&dtest,"test")];// overall configuration for training/evaluationlet params = parameters::TrainingParametersBuilder::default().dtrain(&dtrain)// dataset to train with.boost_rounds(2)// number of training iterations.booster_params(booster_params)// model parameters.evaluation_sets(Some(evaluation_sets))// optional datasets to evaluate against in each iteration.build().unwrap();// train model, and print evaluation datalet bst =Booster::train(&params).unwrap();println!("{:?}", bst.predict(&dtest).unwrap());}

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