지능형 데이터 분석 및 예측을 달성하기 위해 Java에서 기계 학습 라이브러리를 사용하는 방법은 무엇입니까?

import weka.core.Instances;
import weka.filters.Filter;
import weka.filters.unsupervised.attribute.Normalize;
import weka.filters.unsupervised.attribute.StringToWordVector;

public class DataPreprocessing {
    public static void main(String[] args) throws Exception {
        // 读取数据
        Instances data = new Instances(new FileReader("data.arff"));

        // 文本数据预处理，将文本转化为词向量
        StringToWordVector filter = new StringToWordVector();
        filter.setInputFormat(data);
        Instances vectorizedData = Filter.useFilter(data, filter);

        // 特征标准化
        Normalize normalize = new Normalize();
        normalize.setInputFormat(vectorizedData);
        Instances normalizedData = Filter.useFilter(vectorizedData, normalize);

        // 输出预处理后的数据
        System.out.println(normalizedData);
    }
}

import weka.core.Instances;
import weka.classifiers.Classifier;
import weka.classifiers.Evaluation;
import weka.classifiers.functions.LinearRegression;

public class ModelTraining {
    public static void main(String[] args) throws Exception {
        // 读取训练数据
        Instances trainingData = new Instances(new FileReader("train.arff"));
        trainingData.setClassIndex(trainingData.numAttributes() - 1);

        // 构建线性回归模型
        Classifier model = new LinearRegression();
        model.buildClassifier(trainingData);

        // 评估模型
        Evaluation evaluation = new Evaluation(trainingData);
        evaluation.evaluateModel(model, trainingData);

        // 输出模型的评估指标
        System.out.println(evaluation.toSummaryString());
    }
}

import weka.core.Instances;
import weka.classifiers.Classifier;
import weka.classifiers.functions.LinearRegression;

public class ModelPrediction {
    public static void main(String[] args) throws Exception {
        // 读取测试数据
        Instances testData = new Instances(new FileReader("test.arff"));
        testData.setClassIndex(testData.numAttributes() - 1);

        // 加载训练好的模型
        Classifier model = (Classifier) weka.core.SerializationHelper.read("model.model");

        // 对测试数据进行预测
        for (int i = 0; i < testData.numInstances(); i++) {
            double prediction = model.classifyInstance(testData.instance(i));
            System.out.println("预测结果：" + prediction);
        }
    }
}