This tutorial explains how Scikit-learn pipelines can enhance machine learning workflows by simplifying preprocessing and modeling steps, improving code clarity, ensuring consistency in data preprocessing, assisting with hyperparameter tuning, and organizing your workflow. The tutorial uses the Bank Churn dataset from Kaggle to train a Random Forest Classifier, comparing the traditional data preprocessing and model training method with the more efficient Scikit-learn pipelines and ColumnTransformers approach.
We highlight the process of transforming both categorical and numerical columns individually. After loading the data, we drop irrelevant columns. From the cleaned dataset, we fill missing values for both categorical and numerical features, convert categorical features into integers, and scale numerical features.
Next, we demonstrate Scikit-learn Pipelines Code, where we create two pipelines- one for numerical and one for categorical columns. We apply a simple impute for both pipelines with different strategies and a min-max scaler for normalization in the numerical pipeline, while the original encoder is used to convert categories into numerical values in the categorical pipeline. We merge the two using ColumnTransfomer.
For training and evaluation, we split the data into training and testing subsets with dependent and independent variables converted into NumPy arrays. The conventional training code performs feature selection using ‘SelectKBest’ then feeds the new feature to our Random Forest Classifier model. We train the model on the training set and evaluate on the testing set.
We also use the ‘Pipeline’ function to integrate both training steps into a pipeline. By using this, we achieve similar results but with more efficient and straightforward code.
The tutorial shows how to combine both preprocessing and training pipelines into one for a more efficient workflow.
One advantage of using pipelines is the ability to save them with the model. When necessary, we only load the pipeline object, which is ready to process raw data and provide accurate predictions, saving time and making the machine learning workflow more efficient.
We demonstrate saving the pipeline using the skopes-dev/skops library and show how to load the saved pipeline for future use.
To assess the loaded pipeline performance, we make predictions on the test set and calculate accuracy and F1 scores.
In conclusion, this tutorial demonstrates how Scikit-learn pipelines can enhance machine learning workflows by chaining sequences of data transformation and models. Pipelines can simplify code, ensure consistent data transformation, and make workflows more organized and reproducible.
