Evaluating the Model

In this chapter, we’ll focus on evaluating the performance of the trained neural network on the wine quality dataset. This involves using the test set to assess the model’s predictions and calculate metrics like accuracy, precision, and recall. We'll also visualize the confusion matrix to gain insights into how well the model performs across different classes.

Preparing for Evaluation

Before starting the evaluation process, we need to ensure the following:

1. Set the Model to Evaluation Mode: Use model.eval() to turn off features like dropout and batch normalization, ensuring consistent behavior during evaluation.
2. Disable Gradient Tracking: Use torch.no_grad() to save memory and speed up computations, as gradients are not required during evaluation.

Converting Predictions

The output from the model will be logits (raw scores). To get the predicted class labels, we use torch.argmax to extract the index of the maximum value along the class dimension.

Calculating Metrics

For classification problems, accuracy is a good starting metric. You can also calculate other metrics like precision, recall, and F1-score.

Visualizing Performance: Confusion Matrix

A confusion matrix provides deeper insights into the model's performance by showing how many samples were correctly or incorrectly classified for each class.

Full Implementation

Here’s the complete implementation of the evaluation process:

Interpreting the Results

1. Accuracy: The overall percentage of correct predictions. A high accuracy indicates good performance, but it may not tell the full story, especially for imbalanced datasets.
2. Confusion Matrix: Use this to check for specific classes where the model struggles (e.g., confusing one class for another).
3. Next Steps: If the model’s performance is unsatisfactory:
   • Consider tuning hyperparameters.
   • Analyze the confusion matrix for specific patterns or weaknesses.
   • Experiment with a more complex architecture or additional data preprocessing.