java框架如何利用人工智能处理复杂业务逻辑？

// 导入必要的库
import org.apache.spark.ml.classification.LogisticRegression;
import org.apache.spark.ml.evaluation.BinaryClassificationEvaluator;
import org.apache.spark.ml.feature.VectorAssembler;
import org.apache.spark.ml.linalg.Vectors;
import org.apache.spark.ml.Pipeline;
import org.apache.spark.ml.PipelineModel;
import org.apache.spark.ml.tuning.CrossValidator;
import org.apache.spark.ml.tuning.CrossValidatorModel;
import org.apache.spark.ml.tuning.ParamGridBuilder;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SparkSession;

// 创建 SparkSession
SparkSession spark = SparkSession.builder().appName("FraudDetection").getOrCreate();

// 加载并准备数据
Dataset<Row> data = spark.read().parquet("path/to/data.parquet");
data = new VectorAssembler().setInputCols(new String[]{"age", "income", ...}).setOutputCol("features").transform(data);

// 划分数据为训练集和测试集
Dataset<Row>[] splits = data.randomSplit(new double[]{0.7, 0.3});
Dataset<Row> train = splits[0];
Dataset<Row> test = splits[1];

// 创建机器学习管道
Pipeline pipeline = new Pipeline().setStages(new PipelineStage[]{
        new LogisticRegression().setLabelCol("label").setFeaturesCol("features"),
        new VectorAssembler().setInputCols(new String[]{"age", "income", ...}).setOutputCol("features")
});

// 超参数调优
ParamGridBuilder paramGrid = new ParamGridBuilder().build();
CrossValidator cv = new CrossValidator().setEstimator(pipeline).setEvaluator(new BinaryClassificationEvaluator()).setParamGrid(paramGrid);

// 交叉验证以获取最佳模型
CrossValidatorModel cvModel = cv.fit(train);

// 评估最佳模型
LogisticRegressionModel bestModel = (LogisticRegressionModel) cvModel.bestModel();

// 保存模型
bestModel.save("path/to/model.bin");

// 部署模型
ModelLoader.load("path/to/model.bin").transform(test);