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Update TUTORIAL_pytorch_regression.ipynb
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mincasurong authored Jun 11, 2024
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"4. **Model Building:** Creating a neural network model with fully connected layers and dropout for regularization.\n",
"5. **Training the Model:** Training the model and using early stopping to prevent overfitting.\n",
"6. **Evaluating the Model:** Evaluating the model using Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared score (R2).\n",
"7. **Visualization:** Visualizing the training loss and the predictions vs actual values."
"7. **Visualization:** Visualizing the training loss and the predictions vs actual values.\n",
"\n",
"### References:\n",
"- Variance Inflation Factor (VIF): [Wikipedia](https://en.wikipedia.org/wiki/Variance_inflation_factor)\n",
"- Mean Squared Error (MSE): [Wikipedia](https://en.wikipedia.org/wiki/Mean_squared_error)\n",
"- Mean Absolute Error (MAE): [Wikipedia](https://en.wikipedia.org/wiki/Mean_absolute_error)\n",
"- R-squared Score (R2): [Wikipedia](https://en.wikipedia.org/wiki/Coefficient_of_determination)\n",
"\n",
"### Variance Inflation Factor (VIF)\n",
"Variance Inflation Factor (VIF) is a measure of multicollinearity among predictor variables in a regression model. Multicollinearity occurs when predictor variables are highly correlated with each other, which can inflate the variance of the coefficient estimates and make the model unstable and difficult to interpret. A VIF value greater than 5 indicates high multicollinearity.\n",
"\n",
"### Mean Squared Error (MSE)\n",
"Mean Squared Error (MSE) is a measure of the average squared difference between the actual and predicted values. It is used to evaluate the performance of regression models. A lower MSE indicates a better fit of the model to the data.\n",
"\n",
"### Mean Absolute Error (MAE)\n",
"Mean Absolute Error (MAE) is a measure of the average absolute difference between the actual and predicted values. It is also used to evaluate the performance of regression models. A lower MAE indicates a better fit of the model to the data.\n",
"\n",
"### R-squared Score (R2)\n",
"R-squared Score (R2) is a measure of the proportion of variance in the dependent variable that is predictable from the independent variables. It ranges from 0 to 1, with a higher value indicating a better fit of the model to the data."
]
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