How do you compare neural network performance?

1 Training and testing data

The first step to compare neural network performance is to split your data into training and testing sets. Training data is used to train the model, while testing data is used to evaluate how well the model generalizes to unseen data. The split ratio depends on the size and complexity of your data and model, but a common choice is 80% for training and 20% for testing. You can also use cross-validation techniques to reduce the variance and bias of your performance estimates.

2 Performance metrics

The next step is to choose the appropriate performance metrics for your neural network. Performance metrics are numerical values that measure how well the model achieves its objectives, such as accuracy, precision, recall, F1-score, ROC curve, AUC, MSE, MAE, R2, etc. The choice of metrics depends on the type and purpose of your neural network, such as classification, regression, clustering, etc. For example, accuracy is a common metric for classification tasks, while MSE is a common metric for regression tasks. You should also consider the trade-offs and limitations of different metrics, such as the imbalance between false positives and false negatives, or the sensitivity to outliers.

3 Baseline models

Another way to compare neural network performance is to use baseline models as references. Baseline models are simple or standard models that serve as benchmarks for your neural network. For example, you can use a random classifier, a logistic regression, or a decision tree as baseline models for classification tasks. Baseline models help you to assess the improvement and complexity of your neural network, and to identify the areas where it can be further optimized.

4 Hyperparameter tuning

One of the most important and challenging aspects of comparing neural network performance is hyperparameter tuning. Hyperparameters are the parameters that control the architecture and behavior of your neural network, such as the number and size of layers, the activation functions, the learning rate, the regularization, the dropout, etc. Hyperparameters have a significant impact on the performance and efficiency of your neural network, but they are not learned by the model, so you have to find the optimal values manually or automatically. Hyperparameter tuning involves searching for the best combination of hyperparameters that maximize your performance metrics, using methods such as grid search, random search, Bayesian optimization, etc.

5 Model selection and validation

The final step to compare neural network performance is to select and validate the best model for your project. Model selection is the process of choosing the best model among the candidates based on your performance metrics and hyperparameters. Model validation is the process of verifying that your model is reliable and robust, using methods such as hold-out validation, k-fold validation, bootstrapping, etc. Model selection and validation help you to avoid overfitting and underfitting, which are common problems that affect the performance of your neural network. Overfitting occurs when your model learns too much from the training data and fails to generalize to new data. Underfitting occurs when your model learns too little from the training data and fails to capture the underlying patterns.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?