The error rate refers to the proportion of prediction errors in the prediction. The calculation formula is generally: 1 – Accuracy (%)

The trained model can generally be used to measure the error rate of a model in a data set, where three numbers are important:

• Bayes Optimal Error: An ideal unmeasurable limit value that can be used to approximate the human error rate in image recognition.
• Train Error: The error rate of the model used on the Train Set;
• Dev Error: The error rate of the model used on the Dev Set.

Strategies to reduce error rates

1) Reduce Bias

• Try a larger model, such as a neural network with more layers, more neurons, etc.
• Extend training time;
• Adjust the optimization algorithm, such as trying Momentum, RMS Prop, ADOM, etc.;
• Switch to neural network models such as CNN and RNN.

2) Reduce Variance

• More data added;
• Add constraints to make the fitted function smoother;
• Switch to CNN and RNN models.

References:

【1】Machine Learning Strategy (2) – Error Rate