TextClassificationModelMetrics¶

class oci.ai_language.models.TextClassificationModelMetrics(**kwargs)¶

Bases: object

Model level text classification metrics

Methods

__init__(**kwargs) Initializes a new TextClassificationModelMetrics object with values from keyword arguments.

Attributes

`accuracy`	[Required] Gets the accuracy of this TextClassificationModelMetrics.
`macro_f1`	[Required] Gets the macro_f1 of this TextClassificationModelMetrics.
`macro_precision`	[Required] Gets the macro_precision of this TextClassificationModelMetrics.
`macro_recall`	[Required] Gets the macro_recall of this TextClassificationModelMetrics.
`micro_f1`	[Required] Gets the micro_f1 of this TextClassificationModelMetrics.
`micro_precision`	[Required] Gets the micro_precision of this TextClassificationModelMetrics.
`micro_recall`	[Required] Gets the micro_recall of this TextClassificationModelMetrics.
`weighted_f1`	Gets the weighted_f1 of this TextClassificationModelMetrics.
`weighted_precision`	Gets the weighted_precision of this TextClassificationModelMetrics.
`weighted_recall`	Gets the weighted_recall of this TextClassificationModelMetrics.

__init__(**kwargs)¶

Initializes a new TextClassificationModelMetrics object with values from keyword arguments. The following keyword arguments are supported (corresponding to the getters/setters of this class):

Parameters:

accuracy (float) – The value to assign to the accuracy property of this TextClassificationModelMetrics.
micro_f1 (float) – The value to assign to the micro_f1 property of this TextClassificationModelMetrics.
micro_precision (float) – The value to assign to the micro_precision property of this TextClassificationModelMetrics.
micro_recall (float) – The value to assign to the micro_recall property of this TextClassificationModelMetrics.
macro_f1 (float) – The value to assign to the macro_f1 property of this TextClassificationModelMetrics.
macro_precision (float) – The value to assign to the macro_precision property of this TextClassificationModelMetrics.
macro_recall (float) – The value to assign to the macro_recall property of this TextClassificationModelMetrics.
weighted_f1 (float) – The value to assign to the weighted_f1 property of this TextClassificationModelMetrics.
weighted_precision (float) – The value to assign to the weighted_precision property of this TextClassificationModelMetrics.
weighted_recall (float) – The value to assign to the weighted_recall property of this TextClassificationModelMetrics.

accuracy¶

[Required] Gets the accuracy of this TextClassificationModelMetrics. The fraction of the labels that were correctly recognised .

Returns:	The accuracy of this TextClassificationModelMetrics.
Return type:	float

macro_f1¶

[Required] Gets the macro_f1 of this TextClassificationModelMetrics. F1-score, is a measure of a model’s accuracy on a dataset

Returns:	The macro_f1 of this TextClassificationModelMetrics.
Return type:	float

macro_precision¶

[Required] Gets the macro_precision of this TextClassificationModelMetrics. Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)

Returns:	The macro_precision of this TextClassificationModelMetrics.
Return type:	float

macro_recall¶

[Required] Gets the macro_recall of this TextClassificationModelMetrics. Measures the model’s ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.

Returns:	The macro_recall of this TextClassificationModelMetrics.
Return type:	float

micro_f1¶

[Required] Gets the micro_f1 of this TextClassificationModelMetrics. F1-score, is a measure of a model’s accuracy on a dataset

Returns:	The micro_f1 of this TextClassificationModelMetrics.
Return type:	float

micro_precision¶

[Required] Gets the micro_precision of this TextClassificationModelMetrics. Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)

Returns:	The micro_precision of this TextClassificationModelMetrics.
Return type:	float

micro_recall¶

[Required] Gets the micro_recall of this TextClassificationModelMetrics. Measures the model’s ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.

Returns:	The micro_recall of this TextClassificationModelMetrics.
Return type:	float

weighted_f1¶

Gets the weighted_f1 of this TextClassificationModelMetrics. F1-score, is a measure of a model’s accuracy on a dataset

Returns:	The weighted_f1 of this TextClassificationModelMetrics.
Return type:	float

weighted_precision¶

Gets the weighted_precision of this TextClassificationModelMetrics. Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)

Returns:	The weighted_precision of this TextClassificationModelMetrics.
Return type:	float

weighted_recall¶

Gets the weighted_recall of this TextClassificationModelMetrics. Measures the model’s ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.

Returns:	The weighted_recall of this TextClassificationModelMetrics.
Return type:	float