Python Package for Evaluating Regression Models With Single Line of Code
Utility package for practitioners to evaluate the different machine learning algorithms in an amateur fashion by applying it to their everyday predictive regression problems.
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I always believe in democratizing AI and machine learning, and spreading the knowledge in such a way, to cater the larger audiences in general, to harness the power of AI. An attempt inline to this is the development of the python package “regressormetricgraphplot” that is aimed to help users plot the evaluation metric graph with single line code for different widely used regression model metrics comparing them at a glance. With this utility package, it also significantly lowers the barrier for the practitioners to evaluate the different machine learning algorithms in an amateur fashion by applying it to their everyday predictive regression problems.
Before we dwell into the package details, let’s understand a few basic concepts in simple layman terms.
In general, the modeling pipeline involves the pre-processing stage, fitting the machine learning algorithms, and followed by their evaluation. In the figure below, as an example the modeling steps for ensemble learning is depicted. The block A includes the data processing like cleaning, wrangling, aggregation, deriving new features, feature selection, etc. The block B & C depicts the ensemble learning where the pre-processed data is input to the individual models in Layer-1 which are evaluated and tuned. The input to Layer-2 includes predictions from the previous Layer-1 where then the voting ensemble scheme is used to derive the final predictions. The results are combined using the average. Finally, the block D shows the model evaluation and result interpretation. The data is split (70:30 ratio) into training and testing data. The three standalone ML algorithms namely Linear Regression, Random Forest and XGBoost were used. All the models were created with tuned parameters, and then finally a Voting Regression model is used.
Fig: Modeling Pipeline Ensemble Learning Example
Different regression metrics were used for evaluation. Let’s discuss each of them with their formulae, and corresponding simple explanation.
A voting regressor is an ensemble meta-estimator that fits base regressors each on the whole dataset. It then averages the individual predictions to form a final prediction as shown below.
Installation With Anaconda
If you installed your Python with Anaconda you can run the following commands to get started:
Comprehensive demonstrations can be found in the Demo.ipynb file.
If there’s some metrics implementation you would like to add or add in some examples feel free to do so. You can reach me at firstname.lastname@example.org
Happy learning and keep sharing knowledge!
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