How can you interpret neural network results as a data scientist?

1 Why interpretability matters

Interpretability is the ability to explain how a neural network makes predictions or decisions based on the input data. It is important for data scientists for several reasons. First, interpretability can help you understand the strengths and weaknesses of your model, and identify potential sources of bias or error. Second, interpretability can help you communicate your results to stakeholders, customers, or regulators, and justify your choices and assumptions. Third, interpretability can help you improve your model by finding new features, tuning hyperparameters, or applying regularization.

2 How to measure accuracy and error

One of the simplest ways to interpret neural network results is to measure the accuracy and error of your model on the training and testing data. Accuracy is the proportion of correct predictions or classifications, while error is the difference between the actual and predicted values. You can use various metrics to quantify accuracy and error, such as mean squared error, mean absolute error, accuracy score, precision, recall, or F1-score. These metrics can help you evaluate how well your model fits the data, and how generalizable it is to new data.

3 How to visualize outputs and weights

Another way to interpret neural network results is to visualize the outputs and weights of your model. Outputs are the values that the neural network produces at each layer or node, while weights are the parameters that determine how the inputs are transformed into outputs. You can use various tools and libraries, such as TensorFlow, PyTorch, or Keras, to access and plot the outputs and weights of your model. Visualizing outputs and weights can help you understand how the neural network learns from the data, and how it reacts to different inputs.

4 How to use feature importance and attribution

A more advanced way to interpret neural network results is to use feature importance and attribution methods. Feature importance is the measure of how much each input feature contributes to the output or prediction of the model. Attribution is the process of assigning credit or blame to each input feature for a specific output or prediction. You can use various methods to calculate feature importance and attribution, such as permutation, Shapley values, or integrated gradients. These methods can help you identify which features are most relevant or influential for your model, and how they affect the outcome.

5 How to use counterfactuals and what-if analysis

A final way to interpret neural network results is to use counterfactuals and what-if analysis. Counterfactuals are hypothetical scenarios that show how the output or prediction of the model would change if some input features were different. What-if analysis is the process of exploring different counterfactuals and comparing their effects on the model. You can use various tools and frameworks, such as What-If Tool, LIME, or SHAP, to generate and analyze counterfactuals and what-if scenarios. These tools can help you understand the sensitivity and robustness of your model, and discover potential biases or anomalies.