Description

Calculate the evaluation data for regression. The evaluation metrics are: SST: Sum of Squared for Total SSE: Sum of Squares for Error SSR: Sum of Squares for Regression R^2: Coefficient of Determination R: Multiple CorrelationBak Coeffient MSE: Mean Squared Error RMSE: Root Mean Squared Error SAE/SAD: Sum of Absolute Error/Difference MAE/MAD: Mean Absolute Error/Difference MAPE: Mean Absolute Percentage Error

Parameters

Name Description Type Required？ Default Value
labelCol Name of the label column in the input table String ✓
predictionCol Column name of prediction. String ✓

Script Example

Code

  1. import numpy as np
  2. import pandas as pd
  3. data = np.array([
  4.     [0, 0],
  5.     [8, 8],
  6.     [1, 2],
  7.     [9, 10],
  8.     [3, 1],
  9.     [10, 7]
  10. ])
  11. df = pd.DataFrame({"pred": data[:, 0], "label": data[:, 1]})
  12. inOp = BatchOperator.fromDataframe(df, schemaStr='pred int, label int')
  14. metrics = EvalRegressionBatchOp().setPredictionCol("pred").setLabelCol("label").linkFrom(inOp).collectMetrics()
  16. print("Total Samples Number:", metrics.getCount())
  17. print("SSE:", metrics.getSse())
  18. print("SAE:", metrics.getSae())
  19. print("RMSE:", metrics.getRmse())
  20. print("R2:", metrics.getR2())

Results

  1. Total Samples Number: 6.0
  2. SSE: 15.0
  3. SAE: 7.0
  4. RMSE: 1.5811388300841898
  5. R2: 0.8282442748091603