Discussing the article: "Mastering Model Interpretation: Gaining Deeper Insight From Your Machine Learning Models"
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Check out the new article: Mastering Model Interpretation: Gaining Deeper Insight From Your Machine Learning Models.
Machine Learning is a complex and rewarding field for anyone of any experience. In this article we dive deep into the inner mechanisms powering the models you build, we explore the intricate world of features,predictions and impactful decisions unravelling the complexities and gaining a firm grasp of model interpretation. Learn the art of navigating tradeoffs , enhancing predictions, ranking feature importance all while ensuring robust decision making. This essential read helps you clock more performance from your machine learning models and extract more value for employing machine learning methodologies.
In this article, our objective is to employ a gradient boosted tree model, readily available in the CatBoost Python library, to conduct price regression analysis. However, a noteworthy challenge emerges at the outset, necessitating a closer examination of the model and the identification of influential features. Before delving into the application of black-box explanation techniques for our model, it is imperative to comprehend the limitations inherent in our black-box model and the rationale behind employing black-box explainers in this context.
Gradient Boosted Trees exhibit commendable performance in classification tasks; nevertheless, they manifest distinct limitations when applied to specific time series regression problems. These trees, belonging to the family of machine learning models, categorize inputs into groups based on the target value. Subsequently, the algorithm computes the average target value within each group and utilizes these group averages for prediction. Notably, these group averages, established during training, remain fixed unless further training is conducted. A critical drawback emerges from this fixed nature, as Gradient Boosted Trees typically struggle to extrapolate trends effectively. When confronted with input values outside its training scope, the model is prone to repetitive predictions, relying on averages derived from known groups that may not accurately capture the underlying trend beyond the observed training range.
Moreover, the model presupposes that similar feature values will yield similar target values, a assumption inconsistent with our collective experience in trading financial instruments. In financial markets, price patterns may exhibit similarity while concluding at disparate points. This divergence challenges the model's assumption that the generative process produces data falling into homogeneous groups. Consequently, the violation of these assumptions introduces bias into our model.
To substantiate these observations, we will conduct a demonstration for readers who may not have independently observed this phenomenon. Our commitment is to ensure a comprehensive understanding for all readers.
Author: Gamuchirai Zororo Ndawana