We have seen what is linear regression, how to make models and algorithms for estimating the parameters of such models, how to measure the loss.
Now we see how to assess how well the considered method should perform in predicting new data, how to select amongst possible models to choose the best performing.
We will first explore the concept of training and test error, how they vary with model complexity and how they might be utilised to form a valid assessment of predictive performance. This leads directly to an important bias-variance tradeoff, which is fundamental to machine learning.
The concepts described in this post are key to all machine learning problems, well-beyond the regression setting. Continue reading “The overfitting problem and the bias vs. variance dilemma”