https://utsavdarlami.github.io/blogs/tags/precision/Recent content in Precision on felladogHugo -- gohugo.ioenSun, 06 Feb 2022 15:36:55 +0545https://utsavdarlami.github.io/blogs/notes/20210224162319-model_evaluations_score/Wed, 24 Feb 2021 16:23:00 +0545https://utsavdarlami.github.io/blogs/notes/20210224162319-model_evaluations_score/references To Read: https://towardsdatascience.com/handling-imbalanced-datasets-in-machine-learning-7a0e84220f28 https://datascience.stackexchange.com/questions/15989/micro-average-vs-macro-average-performance-in-a-multiclass-classification-settin questions ml model evaluation methods Classfication True Positive (TP) Positive class correctly labeled/predicted False Negative (FN) Positive class incorrectly labeled/predicted False Positive (FP) Negative class incorrectly labeled/predicted True Negative (TN) Negative class correctly labeled/predicted Accuracy It is simply a ratio of correctly predicted observation to the total observations. Accuracy = \(\frac{TP + TN}{TP + TN + FN + FP}\) Precision Precision = \(\frac{True Positive}{True Positive + False Positive}\) From all the postive prediction given by our hypothesis/model how many examples were true positive Recall Recall = \(\frac{True Positive}{True Positive + False Negative}\) From all the postive examples how many examples were correctly classified by our hypothesis/model F1 Score A harmonic mean between recall and precision