ROC曲線

ROC曲線

受信者操作特性 (Receiver Operating Characteristic) 曲線

もともとレーダーシステムの通信工学理論として開発されたものであり、レーダー信号のノイズの中から敵機の存在を検出するための方法として開発された方法

https://gyazo.com/eb2b18f879fa3377bee2aa9987b605ab

横軸は、何らかの陽性者を判定するための数値

大きければ大きいほど陽性っぽいような数値

そして、この分布は真値の陽性者と陰性者の分布を意味する

横軸に偽陽性率、

縦軸に敏感度をプロットし、

カットオフポイントを媒介変数として大から小へと変化させる

と、下の図のような曲線が描かれる

https://gyazo.com/ee59a981987fc43b273b949d5fd7d4b5

横軸 偽陽性率 = 1- specificity

縦軸 敏感度 = sensitivity

どっちも、予測側を分母にしたときの割合のことをいってるね

歴史

The ROC curve was first used during World War II for the analysis of radar signals before it was employed in signal detection theory.

ROC曲線は、第二次世界大戦中にレーダー信号の分析に初めて使用され、その後信号検出理論に採用されました。

Following the attack on Pearl Harbor in 1941, the United States army began new research to increase the prediction of correctly detected Japanese aircraft from their radar signals. For these purposes they measured the ability of a radar receiver operator to make these important distinctions, which was called the Receiver Operating Characteristic.

In the 1950s, ROC curves were employed in psychophysics to assess human (and occasionally non-human animal) detection of weak signals.

1950年代には、心理物理学でROC曲線が、人間（時には人間以外の動物）の弱い信号の検出を評価するために採用されました。

In medicine, ROC analysis has been extensively used in the evaluation of diagnostic tests.

ROC curves are also used extensively in epidemiology and medical research and are frequently mentioned in conjunction with evidence-based medicine.

In radiology, ROC analysis is a common technique to evaluate new radiology techniques.

In the social sciences, ROC analysis is often called the ROC Accuracy Ratio, a common technique for judging the accuracy of default probability models. ROC curves are widely used in laboratory medicine to assess the diagnostic accuracy of a test, to choose the optimal cut-off of a test and to compare diagnostic accuracy of several tests.

ROC curves also proved useful for the evaluation of machine learning techniques. The first application of ROC in machine learning was by Spackman who demonstrated the value of ROC curves in comparing and evaluating different classification algorithms.

ROC curves are also used in verification of forecasts in meteorology.