Update README.md

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.