Applied machine learning & working with modeling pipelines

Applied machine learning is typically focused on finding a single model that performs well or best on a given dataset.

Effective use of the model will require appropriate preparation of the input data and hyperparameter tuning of the model.

Collectively, the linear sequence of steps required to prepare the data, tune the model, and transform the predictions is called the modeling pipeline. Modern machine learning libraries like the scikit-learn Python library allow this sequence of steps to be defined and used correctly (without data leakage) and consistently (during evaluation and prediction).

Nevertheless, working with modeling pipelines can be confusing to beginners as it requires a shift in perspective of the applied machine learning process.

Read more at: https://machinelearningmastery.com/machine-learning-modeling-pipelines/