Testing and validating
After loading our data, preprocessing it, creating new useful features, checking for outliers and other inconsistent data points, and finally choosing the right metric, we are ready to apply a machine learning algorithm.
A machine learning algorithm, by observing a series of examples and pairing them with their outcome, is able to extract a series of rules that can be successfully generalized to new examples by correctly guessing their resulting outcome. Such is the supervised learning approach, where it applies a series of highly specialized learning algorithms that we expect can correctly predict (and generalize) on any new data.
But how can we correctly apply the learning process in order to achieve the best model for prediction to be generally used with similar yet new data?
In data science, there are some best practices to be followed that can assure you the best results in future generalization of your model to any new data. Let's explain the practice by proceeding...
