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.
Its always important to know :
- Applied machine learning is concerned with more than finding a good performing model; it also requires finding an appropriate sequence of data preparation steps and steps for the post-processing of predictions.
- Collectively, the operations required to address a predictive modeling problem can be considered an atomic unit called a modeling pipeline.
- Approaching applied machine learning through the lens of modeling pipelines requires a change in thinking from evaluating specific model configurations to sequences of transforms and algorithms.