To make sure that the model is evaluated based on how good it is to predict new data points, and not how well it is modeled to the current ones, it is common to split the datasets into one training set and one test set (and sometimes a validation set). Often, overfitting machine learning models have very high accuracy on the data sets they are trained on, but as a data scientist, the goal is usually to predict new data points as precisely as possible. Overfitting is when an algorithm is trained and modeled to fit a set of data points too closely so that it does not generalize well to new data points. However, data scientists have to be aware of the dangers of overfitting, which are more evident in projects where small data sets are used. Neural networks are great at learning trends in both large and small data sets. Step 2: Create a Training and Test Data Set
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