Often in Machine Learning and Data Science, you need to perform a sequence of different transformations of the input data (such as finding a set of features or generating new features) before applying a final estimator. Pipeline gives you a single interface for encapsulating transformers and predictors to simplify the process. Since transformers are usually combined with estimators for preprocessing, using pipeline in scikit-learn can be really useful. To be precise, Pipelines sequentially apply a list of transformers and a final estimator. Therefore, the purpose of the pipeline is to assemble several steps that can be cross-validated while setting different parameters. In this video, we discuss the Python implementation of Pipeline using a polynomial regression example.
#Transformer #Estimator #Pipeline
- Understanding Pipeline in Machine Learning with Scikit-learn (sklearn pipeline) ( Download)
- Scikit-Learn Model Pipeline Tutorial ( Download)
- Building a Machine Learning Pipeline with Python and Scikit-Learn | Step-by-Step Tutorial ( Download)
- Implementing Machine Learninng Pipelines USsing Sklearn And Python ( Download)
- Machine learning pipeline with scikit learn ( Download)
- Have you used pipelines for Machine Learning before #shorts ( Download)
- Sklearn Pipeline Tutorial Full - Advanced Machine Learning Tutorial Pipeline Creation ( Download)
- Scikit-learn Crash Course - Machine Learning Library for Python ( Download)
- MAchine Learning: Understanding the PipeLine from SKlearn ( Download)
- Data Pipelines Explained ( Download)
- Machine Learning Pipelines A-Z | Day 29 | 100 Days of Machine Learning ( Download)
- Introduction to Scikit-Learn pipeline API ( Download)
- Creating Pipelines Using SKlearn- Machine Learning Tutorial ( Download)
- Scikit-Learn Pipeline ( Download)
- THIS is HARDEST MACHINE LEARNING model I've EVER coded ( Download)
