relation of model complexity to dataset size it' important to note that model complexity is intimately tied to the variation of inputs contained in your training datasetthe larger variety of data points your dataset containsthe more complex model you can use without overfitting usuallycollecting more data points will yield more varietyso larger datasets allow building more complex models howeversimply duplicating the same data points or collecting very similar data will not help going back to the boat selling exampleif we saw , more rows of customer dataand all of them complied with the rule "if the customer is older than and has less than children or is not divorcedthen they want to buy boat,we would be much more likely to believe this to be good rule than when it was developed using only the rows in table - having more data and building appropriately more complex models can often work wonders for supervised learning tasks in this bookwe will focus on working with datasets of fixed sizes in the real worldyou often have the ability to decide how much data to collectwhich might be more beneficial than tweaking and tuning your model never underestimate the power of more data supervised machine learning algorithms we will now review the most popular machine learning algorithms and explain how they learn from data and how they make predictions we will also discuss how the concept of model complexity plays out for each of these modelsand provide an oversupervised machine learning algorithms