# Performance Analysis

In the following section, we will first perform error analysis using the accuracy metric as a tool to determine the condition that is affecting (in greater proportion) the performance of the algorithm. Once the model is diagnosed, the hyperparameters can be tuned to improve the overall performance of the algorithm. The final model will be compared to those that were created during the previous chapter in order to determine whether a neural network outperforms the other models.

## Error Analysis

Using the accuracy score calculated in *Activity 5.01*, *Training an MLP for Our Census Income Dataset*, we can calculate the error rates for each of the sets and compare them against one another to diagnose the condition that is affecting the model. To do so, a Bayes error equal to 1% will be assumed, considering that other models in the previous chapter were able to achieve an accuracy level of over 97%: