Package com.oracle.bmc.ailanguage.model

Class TextClassificationModelMetrics

  • @Generated(value="OracleSDKGenerator",
           comments="API Version: 20221001")
public final class TextClassificationModelMetrics
extends com.oracle.bmc.http.client.internal.ExplicitlySetBmcModel
    Model level text classification metrics
    Note: Objects should always be created or deserialized using the TextClassificationModelMetrics.Builder.

    This model distinguishes fields that are null because they are unset from fields that are explicitly set to null. This is done in the setter methods of the TextClassificationModelMetrics.Builder, which maintain a set of all explicitly set fields called TextClassificationModelMetrics.Builder.__explicitlySet__. The hashCode() and equals(Object) methods are implemented to take the explicitly set fields into account. The constructor, on the other hand, does not take the explicitly set fields into account (since the constructor cannot distinguish explicit null from unset null).

    • Constructor Detail

      • TextClassificationModelMetrics

        @Deprecated
@ConstructorProperties({"accuracy","microF1","microPrecision","microRecall","macroF1","macroPrecision","macroRecall","weightedF1","weightedPrecision","weightedRecall"})
public TextClassificationModelMetrics​(Float accuracy,
                                      Float microF1,
                                      Float microPrecision,
                                      Float microRecall,
                                      Float macroF1,
                                      Float macroPrecision,
                                      Float macroRecall,
                                      Float weightedF1,
                                      Float weightedPrecision,
                                      Float weightedRecall)
        Deprecated.

    • Method Detail

      • getAccuracy

        public Float getAccuracy()
        The fraction of the labels that were correctly recognised .
        Returns:
        the value

      • getMicroF1

        public Float getMicroF1()
        F1-score, is a measure of a model\u2019s accuracy on a dataset
        Returns:
        the value

      • getMicroPrecision

        public Float getMicroPrecision()
        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 value

      • getMicroRecall

        public Float getMicroRecall()
        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 value

      • getMacroF1

        public Float getMacroF1()
        F1-score, is a measure of a model\u2019s accuracy on a dataset
        Returns:
        the value

      • getMacroPrecision

        public Float getMacroPrecision()
        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 value

      • getMacroRecall

        public Float getMacroRecall()
        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 value

      • getWeightedF1

        public Float getWeightedF1()
        F1-score, is a measure of a model\u2019s accuracy on a dataset
        Returns:
        the value

      • getWeightedPrecision

        public Float getWeightedPrecision()
        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 value

      • getWeightedRecall

        public Float getWeightedRecall()
        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 value

      • toString

        public String toString()
        Overrides:
        toString in class com.oracle.bmc.http.client.internal.ExplicitlySetBmcModel

      • toString

        public String toString​(boolean includeByteArrayContents)
        Return a string representation of the object.
        Parameters:
        includeByteArrayContents - true to include the full contents of byte arrays
        Returns:
        string representation

      • equals

        public boolean equals​(Object o)
        Overrides:
        equals in class com.oracle.bmc.http.client.internal.ExplicitlySetBmcModel

      • hashCode

        public int hashCode()
        Overrides:
        hashCode in class com.oracle.bmc.http.client.internal.ExplicitlySetBmcModel