R: Confusion Matrix

hanaml.Confusion.matrix {hana.ml.r}

R Documentation

Confusion Matrix

Description

Compute confusion matrix to evaluate the accuracy of a classification.

Usage

hanaml.Confusion.matrix(conn.context,
                       data,
                       key,
                       label.true = NULL,
                       label.pred = NULL,
                       beta = NULL)

Arguments

`conn.context`	`ConnectionContext` The connection to the SAP HANA system.
`data`	`DataFrame` DataFrame containing the data.
`key`	`character` Name of the ID column.
`label.true`	`character, optional` Name of the original label column. If not given, defaults to the second columm.
`label.pred`	`character, optional)` Name of the predicted label column. If not given, defaults to the third columm.
`beta`	`double, optional` Parameter used to compute the F-Beta score. Defaults to 1.

Value

DataFrame
Confusion matrix, structured as follows:
- Original label: with same name and data type as it is in data.
- Predicted label: with same name and data type as it is in data.
- Count: type INTEGER, the number of data points with the corresponding combination of predicted and original label.
The DataFrame is sorted by (original label, predicted label) in descending order. NOTE: The data type of the original.label column and predict.label column must be the same.
DataFrame
Classfication report, structured as follows:
- Class: type NVARCHAR(100), class name
- Recall: type DOUBLE, the recall of each class
- Precision: type DOUBLE, the precision of each class
- F_MEASURE: type DOUBLE, the F_measure of each class
- SUPPORT: type INTEGER, the support - sample number in each class

Examples

## Not run: 
DataFrame df to calculate the confusion matrix:
>df$Collect()
      ID  ORIGINAL  PREDICT
   0   1         1        1
   1   2         1        1
   2   3         1        1
   3   4         1        2
   4   5         1        1
   5   6         2        2
   6   7         2        1
   7   8         2        2
   8   9         2        2
   9  10         2        2

Calculate the confusion matrix:

> cm, cr <- hanaml.Confusion.matrix(connection.context = conn, data = df,
                                   key = "ID", label.true = "ORIGINAL",
                                   label.pred = "PREDICT")

Return:

> cm$Collect()
      ORIGINAL  PREDICT  COUNT
   0         1        1      4
   1         1        2      1
   2         2        1      1
   3         2        2      4

> cr$Collect()
     CLASS  RECALL  PRECISION  F_MEASURE  SUPPORT
   0     1     0.8        0.8        0.8        5
   1     2     0.8        0.8        0.8        5

## End(Not run)

[Package hana.ml.r version 1.0.8 Index]