com.google.api.services.bigquery.model.BinaryConfusionMatrix Maven / Gradle / Ivy
/*
* Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
* in compliance with the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software distributed under the License
* is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
* or implied. See the License for the specific language governing permissions and limitations under
* the License.
*/
/*
* This code was generated by https://github.com/googleapis/google-api-java-client-services/
* Modify at your own risk.
*/
package com.google.api.services.bigquery.model;
/**
* Confusion matrix for binary classification models.
*
* This is the Java data model class that specifies how to parse/serialize into the JSON that is
* transmitted over HTTP when working with the BigQuery API. For a detailed explanation see:
* https://developers.google.com/api-client-library/java/google-http-java-client/json
*
*
* @author Google, Inc.
*/
@SuppressWarnings("javadoc")
public final class BinaryConfusionMatrix extends com.google.api.client.json.GenericJson {
/**
* The fraction of predictions given the correct label.
* The value may be {@code null}.
*/
@com.google.api.client.util.Key
private java.lang.Double accuracy;
/**
* The equally weighted average of recall and precision.
* The value may be {@code null}.
*/
@com.google.api.client.util.Key
private java.lang.Double f1Score;
/**
* Number of false samples predicted as false.
* The value may be {@code null}.
*/
@com.google.api.client.util.Key @com.google.api.client.json.JsonString
private java.lang.Long falseNegatives;
/**
* Number of false samples predicted as true.
* The value may be {@code null}.
*/
@com.google.api.client.util.Key @com.google.api.client.json.JsonString
private java.lang.Long falsePositives;
/**
* Threshold value used when computing each of the following metric.
* The value may be {@code null}.
*/
@com.google.api.client.util.Key
private java.lang.Double positiveClassThreshold;
/**
* The fraction of actual positive predictions that had positive actual labels.
* The value may be {@code null}.
*/
@com.google.api.client.util.Key
private java.lang.Double precision;
/**
* The fraction of actual positive labels that were given a positive prediction.
* The value may be {@code null}.
*/
@com.google.api.client.util.Key
private java.lang.Double recall;
/**
* Number of true samples predicted as false.
* The value may be {@code null}.
*/
@com.google.api.client.util.Key @com.google.api.client.json.JsonString
private java.lang.Long trueNegatives;
/**
* Number of true samples predicted as true.
* The value may be {@code null}.
*/
@com.google.api.client.util.Key @com.google.api.client.json.JsonString
private java.lang.Long truePositives;
/**
* The fraction of predictions given the correct label.
* @return value or {@code null} for none
*/
public java.lang.Double getAccuracy() {
return accuracy;
}
/**
* The fraction of predictions given the correct label.
* @param accuracy accuracy or {@code null} for none
*/
public BinaryConfusionMatrix setAccuracy(java.lang.Double accuracy) {
this.accuracy = accuracy;
return this;
}
/**
* The equally weighted average of recall and precision.
* @return value or {@code null} for none
*/
public java.lang.Double getF1Score() {
return f1Score;
}
/**
* The equally weighted average of recall and precision.
* @param f1Score f1Score or {@code null} for none
*/
public BinaryConfusionMatrix setF1Score(java.lang.Double f1Score) {
this.f1Score = f1Score;
return this;
}
/**
* Number of false samples predicted as false.
* @return value or {@code null} for none
*/
public java.lang.Long getFalseNegatives() {
return falseNegatives;
}
/**
* Number of false samples predicted as false.
* @param falseNegatives falseNegatives or {@code null} for none
*/
public BinaryConfusionMatrix setFalseNegatives(java.lang.Long falseNegatives) {
this.falseNegatives = falseNegatives;
return this;
}
/**
* Number of false samples predicted as true.
* @return value or {@code null} for none
*/
public java.lang.Long getFalsePositives() {
return falsePositives;
}
/**
* Number of false samples predicted as true.
* @param falsePositives falsePositives or {@code null} for none
*/
public BinaryConfusionMatrix setFalsePositives(java.lang.Long falsePositives) {
this.falsePositives = falsePositives;
return this;
}
/**
* Threshold value used when computing each of the following metric.
* @return value or {@code null} for none
*/
public java.lang.Double getPositiveClassThreshold() {
return positiveClassThreshold;
}
/**
* Threshold value used when computing each of the following metric.
* @param positiveClassThreshold positiveClassThreshold or {@code null} for none
*/
public BinaryConfusionMatrix setPositiveClassThreshold(java.lang.Double positiveClassThreshold) {
this.positiveClassThreshold = positiveClassThreshold;
return this;
}
/**
* The fraction of actual positive predictions that had positive actual labels.
* @return value or {@code null} for none
*/
public java.lang.Double getPrecision() {
return precision;
}
/**
* The fraction of actual positive predictions that had positive actual labels.
* @param precision precision or {@code null} for none
*/
public BinaryConfusionMatrix setPrecision(java.lang.Double precision) {
this.precision = precision;
return this;
}
/**
* The fraction of actual positive labels that were given a positive prediction.
* @return value or {@code null} for none
*/
public java.lang.Double getRecall() {
return recall;
}
/**
* The fraction of actual positive labels that were given a positive prediction.
* @param recall recall or {@code null} for none
*/
public BinaryConfusionMatrix setRecall(java.lang.Double recall) {
this.recall = recall;
return this;
}
/**
* Number of true samples predicted as false.
* @return value or {@code null} for none
*/
public java.lang.Long getTrueNegatives() {
return trueNegatives;
}
/**
* Number of true samples predicted as false.
* @param trueNegatives trueNegatives or {@code null} for none
*/
public BinaryConfusionMatrix setTrueNegatives(java.lang.Long trueNegatives) {
this.trueNegatives = trueNegatives;
return this;
}
/**
* Number of true samples predicted as true.
* @return value or {@code null} for none
*/
public java.lang.Long getTruePositives() {
return truePositives;
}
/**
* Number of true samples predicted as true.
* @param truePositives truePositives or {@code null} for none
*/
public BinaryConfusionMatrix setTruePositives(java.lang.Long truePositives) {
this.truePositives = truePositives;
return this;
}
@Override
public BinaryConfusionMatrix set(String fieldName, Object value) {
return (BinaryConfusionMatrix) super.set(fieldName, value);
}
@Override
public BinaryConfusionMatrix clone() {
return (BinaryConfusionMatrix) super.clone();
}
}