Confusion Matrixis a matrix that illustrates the performance of a classification model when exposed to unseen data. This matrix helps us to identify how the model is performing on test set. From this matrix, many other scores are calculated such as Accuracy, Recall, Precision, F1-score, etc. It is important one should know where to use which type of score as it depends on the application.
There are two classes: Class 1 and Class 2
Class 1:Positive
Class 2: Negative
Positive: Observation is True (eg. Picture is a dog)
Negative: Observation is False (eg. Picture is not a dog)
T.P.(True Positive): Truth and Prediction both are Positive
T.N.(True Negative): Truth and Prediction both are Negative
F.P.(False Positive): Truth is Negative but Prediction is Positive
F.N.(False Negative): Truth is Positive but Prediction is Negative
Accuracy:
Accuracy is the ratio of sum of True Positive(T.P.) and True Negative(T.N.) to the sum of the matrix elements.
Precision:
Precision is defined as the ratio of True Positive(T.P) to the sum of True Positive(T.P) and False Positive(F.P)
Recall:
Recall is defined as the ratio of True Positive(T.P) to the sum of True Positive(T.P) and False Negative(F.N)
High recall, low precision:This means that most of the positive examples are correctly recognized (low FN) but there are a lot of false positives.
Low recall, high precision:This shows that we miss a lot of positive examples (high FN) but those we predict as positive are indeed positive (low FP)
F1-score:
Since we have two measures (Precision and Recall) it helps to have a measurement that represents both of them. We calculate an F-measure which uses Harmonic Mean in place of Arithmetic Mean as it punishes the extreme values more.
The F-Measure will always be nearer to the smaller value of Precision or Recall.
Exercise
Accuracy
Accuracy = (TP + TN) / (TP + TN + FP + FN) = (100 + 50) /(100 + 5 + 10 + 50) = 0.90Precision
Precision tells us about when it predicts yes, how often is it correct.Precision = TP / (TP + FP)=100/ (100+10) = 0.91
Recall
Recall gives us an idea about when it’s actually yes, how often does it predict yes.
Recall = TP / (TP + FN) = 100 / (100 + 5) = 0.95
F-score
F1-score = (2 * Recall * Precision) / (Recall + Presision) = (2 * 0.95 * 0.91) / (0.91 + 0.95) = 0.9Got any questions?
Email: amarmandal2153@gmail.com
Thank youuuu…
以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持 码农网
猜你喜欢:
本站部分资源来源于网络，本站转载出于传递更多信息之目的，版权归原作者或者来源机构所有，如转载稿涉及版权问题，请联系我们。
游戏编程算法与技巧
【美】Sanjay Madhav / 刘瀚阳 / 电子工业出版社 / 2016-10 / 89
《游戏编程算法与技巧》介绍了大量今天在游戏行业中用到的算法与技术。《游戏编程算法与技巧》是为广大熟悉面向对象编程以及基础数据结构的游戏开发者所设计的。作者采用了一种独立于平台框架的方法来展示开发，包括2D 和3D 图形学、物理、人工智能、摄像机等多个方面的技术。《游戏编程算法与技巧》中内容几乎兼容所有游戏，无论这些游戏采用何种风格、开发语言和框架。 《游戏编程算法与技巧》的每个概念都是用C#......一起来看看 《游戏编程算法与技巧》 这本书的介绍吧!
