One of the key aspects of understanding prediction models is understanding the prediction error. It measures how good at predicting the model is and a simple way to compute is simply comparing the predicted values against the real observed counterparts (assuming a supervised learning scenario). But the job does not end with calculating the error because this might be large and hence it would… Read more →

# Tag Archive for evaluation

# Cross-Validation Strategies

When you are building a prediction model, let’s say a linear regression to keep it simple, you need to be aware of how good at predicting that model is. A common evauation technique, with its origin in the statistical world, is the evaluation of residuals. Residuals are defined as the difference between the predicted and observed values (remember that we use labeled… Read more →

# Predicting with Labeled data

Imagine that you have to implement a model that predicts handwritten numbers and you choose to do it with a Neural Network. You could just trust your instincts and invent both the number of units per layer and the set of Θ values. Applying the Forward Propagation algorithm would suffice to come up with the prediction. Unfortunately, that model would definitely predict with an uncertain accuracy (just as… Read more →