ClassificationMetrics
A collection of classification metrics.
Stub code in ClassificationMetrics.sdsstub
| class ClassificationMetrics {
/**
* Summarize classification metrics on the given data.
*
* @param predicted The predicted target values produced by the classifier.
* @param expected The expected target values.
* @param positiveClass The class to be considered positive. All other classes are considered negative.
*
* @result metrics A table containing the classification metrics.
*/
@Pure
static fun summarize(
predicted: union<Column<Any>, TabularDataset, TimeSeriesDataset>,
expected: union<Column<Any>, TabularDataset, TimeSeriesDataset>,
@PythonName("positive_class") positiveClass: Any
) -> metrics: Table
/**
* Compute the accuracy on the given data.
*
* The accuracy is the proportion of predicted target values that were correct. The **higher** the accuracy, the
* better. Results range from 0.0 to 1.0.
*
* @param predicted The predicted target values produced by the classifier.
* @param expected The expected target values.
*
* @result accuracy The calculated accuracy.
*/
@Pure
static fun accuracy(
predicted: union<Column<Any>, TabularDataset, TimeSeriesDataset>,
expected: union<Column<Any>, TabularDataset, TimeSeriesDataset>
) -> accuracy: Float
/**
* Compute the F₁ score on the given data.
*
* The F₁ score is the harmonic mean of precision and recall. The **higher** the F₁ score, the better the
* classifier. Results range from 0.0 to 1.0.
*
* @param predicted The predicted target values produced by the classifier.
* @param expected The expected target values.
* @param positiveClass The class to be considered positive. All other classes are considered negative.
*
* @result f1Score The calculated F₁ score.
*/
@Pure
@PythonName("f1_score")
static fun f1Score(
predicted: union<Column<Any>, TabularDataset, TimeSeriesDataset>,
expected: union<Column<Any>, TabularDataset, TimeSeriesDataset>,
@PythonName("positive_class") positiveClass: Any
) -> f1Score: Float
/**
* Compute the precision on the given data.
*
* The precision is the proportion of positive predictions that were correct. The **higher** the precision, the
* better the classifier. Results range from 0.0 to 1.0.
*
* @param predicted The predicted target values produced by the classifier.
* @param expected The expected target values.
* @param positiveClass The class to be considered positive. All other classes are considered negative.
*
* @result precision The calculated precision.
*/
@Pure
static fun precision(
predicted: union<Column<Any>, TabularDataset, TimeSeriesDataset>,
expected: union<Column<Any>, TabularDataset, TimeSeriesDataset>,
@PythonName("positive_class") positiveClass: Any
) -> precision: Float
/**
* Compute the recall on the given data.
*
* The recall is the proportion of actual positives that were predicted correctly. The **higher** the recall, the
* better the classifier. Results range from 0.0 to 1.0.
*
* @param predicted The predicted target values produced by the classifier.
* @param expected The expected target values.
* @param positiveClass The class to be considered positive. All other classes are considered negative.
*
* @result recall The calculated recall.
*/
@Pure
static fun recall(
predicted: union<Column<Any>, TabularDataset, TimeSeriesDataset>,
expected: union<Column<Any>, TabularDataset, TimeSeriesDataset>,
@PythonName("positive_class") positiveClass: Any
) -> recall: Float
}
|
accuracy
Compute the accuracy on the given data.
The accuracy is the proportion of predicted target values that were correct. The higher the accuracy, the
better. Results range from 0.0 to 1.0.
Parameters:
Name |
Type |
Description |
Default |
predicted |
union<Column<Any>, TabularDataset, TimeSeriesDataset> |
The predicted target values produced by the classifier. |
- |
expected |
union<Column<Any>, TabularDataset, TimeSeriesDataset> |
The expected target values. |
- |
Results:
Name |
Type |
Description |
accuracy |
Float |
The calculated accuracy. |
Stub code in ClassificationMetrics.sdsstub
| @Pure
static fun accuracy(
predicted: union<Column<Any>, TabularDataset, TimeSeriesDataset>,
expected: union<Column<Any>, TabularDataset, TimeSeriesDataset>
) -> accuracy: Float
|
f1Score
Compute the F₁ score on the given data.
The F₁ score is the harmonic mean of precision and recall. The higher the F₁ score, the better the
classifier. Results range from 0.0 to 1.0.
Parameters:
Name |
Type |
Description |
Default |
predicted |
union<Column<Any>, TabularDataset, TimeSeriesDataset> |
The predicted target values produced by the classifier. |
- |
expected |
union<Column<Any>, TabularDataset, TimeSeriesDataset> |
The expected target values. |
- |
positiveClass |
Any |
The class to be considered positive. All other classes are considered negative. |
- |
Results:
Name |
Type |
Description |
f1Score |
Float |
The calculated F₁ score. |
Stub code in ClassificationMetrics.sdsstub
| @Pure
@PythonName("f1_score")
static fun f1Score(
predicted: union<Column<Any>, TabularDataset, TimeSeriesDataset>,
expected: union<Column<Any>, TabularDataset, TimeSeriesDataset>,
@PythonName("positive_class") positiveClass: Any
) -> f1Score: Float
|
precision
Compute the precision on the given data.
The precision is the proportion of positive predictions that were correct. The higher the precision, the
better the classifier. Results range from 0.0 to 1.0.
Parameters:
Name |
Type |
Description |
Default |
predicted |
union<Column<Any>, TabularDataset, TimeSeriesDataset> |
The predicted target values produced by the classifier. |
- |
expected |
union<Column<Any>, TabularDataset, TimeSeriesDataset> |
The expected target values. |
- |
positiveClass |
Any |
The class to be considered positive. All other classes are considered negative. |
- |
Results:
Name |
Type |
Description |
precision |
Float |
The calculated precision. |
Stub code in ClassificationMetrics.sdsstub
| @Pure
static fun precision(
predicted: union<Column<Any>, TabularDataset, TimeSeriesDataset>,
expected: union<Column<Any>, TabularDataset, TimeSeriesDataset>,
@PythonName("positive_class") positiveClass: Any
) -> precision: Float
|
recall
Compute the recall on the given data.
The recall is the proportion of actual positives that were predicted correctly. The higher the recall, the
better the classifier. Results range from 0.0 to 1.0.
Parameters:
Name |
Type |
Description |
Default |
predicted |
union<Column<Any>, TabularDataset, TimeSeriesDataset> |
The predicted target values produced by the classifier. |
- |
expected |
union<Column<Any>, TabularDataset, TimeSeriesDataset> |
The expected target values. |
- |
positiveClass |
Any |
The class to be considered positive. All other classes are considered negative. |
- |
Results:
Name |
Type |
Description |
recall |
Float |
The calculated recall. |
Stub code in ClassificationMetrics.sdsstub
| @Pure
static fun recall(
predicted: union<Column<Any>, TabularDataset, TimeSeriesDataset>,
expected: union<Column<Any>, TabularDataset, TimeSeriesDataset>,
@PythonName("positive_class") positiveClass: Any
) -> recall: Float
|
summarize
Summarize classification metrics on the given data.
Parameters:
Name |
Type |
Description |
Default |
predicted |
union<Column<Any>, TabularDataset, TimeSeriesDataset> |
The predicted target values produced by the classifier. |
- |
expected |
union<Column<Any>, TabularDataset, TimeSeriesDataset> |
The expected target values. |
- |
positiveClass |
Any |
The class to be considered positive. All other classes are considered negative. |
- |
Results:
Name |
Type |
Description |
metrics |
Table |
A table containing the classification metrics. |
Stub code in ClassificationMetrics.sdsstub
| @Pure
static fun summarize(
predicted: union<Column<Any>, TabularDataset, TimeSeriesDataset>,
expected: union<Column<Any>, TabularDataset, TimeSeriesDataset>,
@PythonName("positive_class") positiveClass: Any
) -> metrics: Table
|