apache-spark - 如何确定逻辑回归spark中的标签和特征？

Question

我正在使用spark mlib，并使用Logistic回归模型进行分类。我点击了这个链接: https://spark.apache.org/docs/2.1.0/ml-classification-regression.html#logistic-regression

 import org.apache.spark.ml.classification.LogisticRegression;
import org.apache.spark.ml.classification.LogisticRegressionModel;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SparkSession;

// Load training data
Dataset<Row> training = spark.read().format("libsvm")
  .load("data/mllib/sample_libsvm_data.txt");

LogisticRegression lr = new LogisticRegression()
  .setMaxIter(10)
  .setRegParam(0.3)
  .setElasticNetParam(0.8);

// Fit the model
LogisticRegressionModel lrModel = lr.fit(training);

// Print the coefficients and intercept for logistic regression
System.out.println("Coefficients: "
  + lrModel.coefficients() + " Intercept: " + lrModel.intercept());

// We can also use the multinomial family for binary classification
LogisticRegression mlr = new LogisticRegression()
        .setMaxIter(10)
        .setRegParam(0.3)
        .setElasticNetParam(0.8)
        .setFamily("multinomial");

// Fit the model
LogisticRegressionModel mlrModel = mlr.fit(training);

如果我将 .csv 作为输入，我不确定该模型如何识别标签和特征？谁能解释一下吗？

Answer 1

因为你从at数据加载libsvm，它由标签index1:value1 index2:value2......组成 如果使用.csv，显然必须指定参数。