@@ -378,7 +378,7 @@ A sample representation of the output in the metadata file is as shown below,
```
<details>
<summary>Appendix : Information on metadata </summary>
<br>
In summary, we extract metadata such as dataset features and model metrics. This part of the section talks about the different types of model metrics and dataset features.
**Model Metrics:**
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@@ -386,10 +386,31 @@ The models in the playground can be broadly classified into two different types:
Classification Model:
|S:NO| Metrics | Functionality |
| ---- | ------ | ------ |
| 1 | accuracy | Accuracy is a metric that measures the percentage of correctly predicted instances in your dataset. It's a straightforward way to evaluate how well your model is performing in terms of getting predictions right. |
| 2 | validation_loss | Validation loss is a metric that quantifies how well your machine learning model fits the training data. |
<divalign="center">
<table>
<thead>
<tr>
<thalign="center">S.no</th>
<thalign="center"> Metrics</th>
<thalign="center">Functionality</th>
</tr>
</thead>
<tbody>
<tr>
<tdalign="left">1</td>
<tdalign="left">Accuracy</td>
<tdalign="left">Accuracy is a metric that measures the percentage of correctly predicted instances in your dataset. It's a straightforward way to evaluate how well your model is performing in terms of getting predictions right.</td>
</tr>
<tr>
<tdalign="left">2</td>
<tdalign="left">validation_loss</td>
<tdalign="left">Validation loss is a metric that quantifies how well your machine learning model fits the training data.</td>
</tr>
</tbody>
</table>
<p></p>
</div>
A sample:
```json
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@@ -406,12 +427,43 @@ A sample:
In conclusion, Accuracy can be used as a guiding metric during model development and hyperparameter tuning. validation_loss is typically used during the training process to monitor whether the model is improving or overfitting. A decreasing validation loss indicates that the model is learning and improving its ability to make predictions. However, if the validation loss starts increasing, it may be a sign of overfitting.
Regressor model:
| S.no | Metrics | Functionality|
| ------ | ------ | ------|
| 1 | Root Mean Square Error | RMSE is a measure of the average deviation (error) between predicted values and actual values in a regression model. RMSE provides a sense of how much the model's predictions deviate from the true values, with lower RMSE values indicating better model performance. |
| 2 | Mean Squared Error | MSE is another metric that quantifies the average squared difference between predicted values and actual values. Unlike RMSE, MSE does not take the square root, so it is sensitive to large errors.|
| 3 | R2 Score | R2 (R-squared) is a metric that represents the proportion of the variance in the dependent variable (target) that is explained by the independent variables (features) in a regression model. It ranges between 0 and 1, with higher values indicating a better fit of the model to the data. A value of 1 indicates a perfect fit.|
| 4 | Adjusted R2 Score | Adjusted R2 is a modification of the R2 score that accounts for the number of independent variables (features) in the model.It penalizes the inclusion of irrelevant features and generally provides a more accurate assessment of model performance, especially in multiple regression.|
<divalign="center">
<table>
<thead>
<tr>
<thalign="center">S.no</th>
<thalign="center"> Metrics</th>
<thalign="center">Functionality</th>
</tr>
</thead>
<tbody>
<tr>
<tdalign="left">1</td>
<tdalign="left">Root Mean Square Error</td>
<tdalign="left">RMSE is a measure of the average deviation (error) between predicted values and actual values in a regression model. RMSE provides a sense of how much the model's predictions deviate from the true values, with lower RMSE values indicating better model performance.</td>
</tr>
<tr>
<tdalign="left">2</td>
<tdalign="left">Mean Squared Error</td>
<tdalign="left">MSE is another metric that quantifies the average squared difference between predicted values and actual values. Unlike RMSE, MSE does not take the square root, so it is sensitive to large errors.</td>
</tr>
<tr>
<tdalign="left">3</td>
<tdalign="left">R2 Score</td>
<tdalign="left">R2 (R-squared) is a metric that represents the proportion of the variance in the dependent variable (target) that is explained by the independent variables (features) in a regression model. It ranges between 0 and 1, with higher values indicating a better fit of the model to the data. A value of 1 indicates a perfect fit.</td>
</tr>
<tr>
<tdalign="left">4</td>
<tdalign="left">Adjusted R2 Score</td>
<tdalign="left">Adjusted R2 is a modification of the R2 score that accounts for the number of independent variables (features) in the model.It penalizes the inclusion of irrelevant features and generally provides a more accurate assessment of model performance, especially in multiple regression</td>
</tr>
</tbody>
</table>
<p></p>
</div>
A sample:
```json
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@@ -429,12 +481,42 @@ In conclusion, RMSE and MSE measure the accuracy of predictions, R2 quantifies t
**Dataset Features:**
| S.no | Metrics | Functionality|
| ------ | ------ | ------|
| 1 | Dataset Name | This is the name or label given to a specific dataset, often indicating its content, source, or purpose. |
| 2 | Description | A brief or detailed explanation of what the dataset contains, its origin, format, and any other relevant information for potential users.|
| 3 | Size | The size of the dataset, typically measured in terms of the number of records, rows, columns, or the total file size in bytes or other appropriate units.|
| 4 | DOI (Digital Object Identifier) or ID | A unique and persistent identifier assigned to the dataset, often used for citation and reference purposes, ensuring its accessibility and traceability. |
<divalign="center">
<table>
<thead>
<tr>
<thalign="center">S.no</th>
<thalign="center">Features</th>
<thalign="center">Functionality</th>
</tr>
</thead>
<tbody>
<tr>
<tdalign="left">1</td>
<tdalign="left">Dataset name</td>
<tdalign="left">This is the name or label given to a specific dataset, often indicating its content, source, or purpose.</td>
</tr>
<tr>
<tdalign="left">2</td>
<tdalign="left">Description</td>
<tdalign="left">A brief or detailed explanation of what the dataset contains, its origin, format, and any other relevant information for potential users.</td>
</tr>
<tr>
<tdalign="left">3</td>
<tdalign="left">Size</td>
<tdalign="left">The size of the dataset, typically measured in terms of the number of records, rows, columns, or the total file size in bytes or other appropriate units.</td>
</tr>
<tr>
<tdalign="left">4</td>
<tdalign="left">DOI (Digital Object Identifier) or ID</td>
<tdalign="left">A unique and persistent identifier assigned to the dataset, often used for citation and reference purposes, ensuring its accessibility and traceability.</td>
