From 1c4cf95a74c7b1efaf52d782a15faa7ba79f3293 Mon Sep 17 00:00:00 2001 From: Marshall Date: Sun, 8 Dec 2024 20:02:51 -0500 Subject: [PATCH] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index ee1cf10..b54b6fe 100644 --- a/README.md +++ b/README.md @@ -207,7 +207,7 @@ An imbalanced dataset is one that has different proportions of target categories There are different options to deal with imbalanced datasets: - Oversampling or undersampling. Instead of sampling with a uniform distribution from the training dataset, we can use other distributions so the model sees a more balanced dataset. - Data augmentation. We can add data in the less frequent categories by modifying existing data in a controlled way. In the example dataset, we could flip the images with illnesses, or add noise to copies of the images in such a way that the illness remains visible. - - Using appropriate metrics. In the example dataset, if we had a model that always made negative predictions, it would achieve a precision of 98%. There are other metrics such as precision, recall, and F-score that describe the accuracy of the model better when using an imbalanced dataset. + - Using appropriate metrics. In the example dataset, if we had a model that always made negative predictions, it would achieve an accuracy of 98%. There are other metrics such as precision, recall, and F-score that describe the accuracy of the model better when using an imbalanced dataset. #### 33) Can you explain the differences between supervised, unsupervised, and reinforcement learning? [[src](https://www.toptal.com/machine-learning/interview-questions)] In supervised learning, we train a model to learn the relationship between input data and output data. We need to have labeled data to be able to do supervised learning.