Update README.md

README.md

@@ -97,15 +97,17 @@ display(results)
 
 <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
 
-maryzhang/hw1-24679-image-dataset
+EricCRX/books-tabular-dataset
 
 This is the training dataset used. 
-It consists of 30 original images used for validation along with 300 synthetic pieces of data from training. 
+It consists of 30 original measurements used for validation along with 300 synthetic pieces of data from training. 
 
 ### Training Procedure
 
 <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-This model was trained with an AutoML process with accuracy as the main metrics. The modelw as trained over 20 epochs with a batch size of 32 images. 
+This model was trained with an AutoML process with accuracy as the main metrics. 
+This model used a max time_limit of 300 seconds to reduce training time and "best_quality" to improve results
+
 
 
 #### Training Hyperparameters
@@ -129,18 +131,23 @@ The testing data was the 'original' split, the 30 original images in this set.
 
 <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
 
-This dataset is evaluating whether the food is Western, "1", or Asian, "0".
+This dataset is evaluating whether the books are hardcovers "1", or softcovers "0"
 
 #### Metrics
 
 <!-- These are the evaluation metrics being used, ideally with a description of why. -->
 
 The testing metric used was accuracy to ensure the highest accuracy of the model possible. 
+Training time was also considered to ensure final models were not computationally infeasible. 
 
 
 ### Results
 
-After training with the initial dataset, this model reached an accuracy of 95% in validation. 
+After training with the initial dataset, this model reached an accuracy of 97% in validation. 
+It also had an individual prediction time of 0.12 seconds making it fast with a high accuracy. 
+
+This validation should not be taken as a metric for robustness. Due to the small dataset, this cannot be confirmed to work for outside mearements. 
+Expanding this dataset could find issues or improvements to this model. 
 
 #### Summary