GoogleCloudAiplatformV1SchemaModelevaluationMetricsClassificationEvaluationMetricsConfidenceMetrics

import type { GoogleCloudAiplatformV1SchemaModelevaluationMetricsClassificationEvaluationMetricsConfidenceMetrics } from "https://googleapis.deno.dev/v1/aiplatform:v1.ts";

interface GoogleCloudAiplatformV1SchemaModelevaluationMetricsClassificationEvaluationMetricsConfidenceMetrics {

confidenceThreshold?: number;

confusionMatrix?: GoogleCloudAiplatformV1SchemaModelevaluationMetricsConfusionMatrix;

f1Score?: number;

f1ScoreAt1?: number;

f1ScoreMacro?: number;

f1ScoreMicro?: number;

falseNegativeCount?: bigint;

falsePositiveCount?: bigint;

falsePositiveRate?: number;

falsePositiveRateAt1?: number;

maxPredictions?: number;

precision?: number;

precisionAt1?: number;

recall?: number;

recallAt1?: number;

trueNegativeCount?: bigint;

truePositiveCount?: bigint;

}

§Properties

confidenceThreshold?: number

[src]

Metrics are computed with an assumption that the Model never returns predictions with score lower than this value.

confusionMatrix?: GoogleCloudAiplatformV1SchemaModelevaluationMetricsConfusionMatrix

[src]

Confusion matrix of the evaluation for this confidence_threshold.

f1Score?: number

[src]

The harmonic mean of recall and precision. For summary metrics, it computes the micro-averaged F1 score.

f1ScoreAt1?: number

[src]

The harmonic mean of recallAt1 and precisionAt1.

f1ScoreMacro?: number

[src]

Macro-averaged F1 Score.

f1ScoreMicro?: number

[src]

Micro-averaged F1 Score.

falseNegativeCount?: bigint

[src]

The number of ground truth labels that are not matched by a Model created label.

falsePositiveCount?: bigint

[src]

The number of Model created labels that do not match a ground truth label.

falsePositiveRate?: number

[src]

False Positive Rate for the given confidence threshold.

falsePositiveRateAt1?: number

[src]

The False Positive Rate when only considering the label that has the highest prediction score and not below the confidence threshold for each DataItem.

maxPredictions?: number

[src]

Metrics are computed with an assumption that the Model always returns at most this many predictions (ordered by their score, descendingly), but they all still need to meet the confidenceThreshold.

precision?: number

[src]

Precision for the given confidence threshold.

precisionAt1?: number

[src]

The precision when only considering the label that has the highest prediction score and not below the confidence threshold for each DataItem.

recall?: number

[src]

Recall (True Positive Rate) for the given confidence threshold.

recallAt1?: number

[src]

The Recall (True Positive Rate) when only considering the label that has the highest prediction score and not below the confidence threshold for each DataItem.

trueNegativeCount?: bigint

[src]

The number of labels that were not created by the Model, but if they would, they would not match a ground truth label.

truePositiveCount?: bigint

[src]

The number of Model created labels that match a ground truth label.