How can a strong understanding of statistics improve your machine learning models?

Antes de inicar con la creación de un modelo de aprendizaje, se debe hacer adquirir conocimiento de la información a través del análisis descriptivo, donde se pueden evitar sesgos, evaluar la calidad de los datos, guiar la elección del modelo adecuado e incluso determinar si es necesario desarrollar un modelo (no todas las soluciones de data son modelos automáticos). Además, según el tipo de modelo que estemos considerando, es importante realizar validaciones en los datos para asegurarnos de que estos cumplan con todos los supuestos necesarios, por último, una vez obtenidos los resultados, se deben aplicar diversos métodos de validación de resultados, los cuales se derivan claramente de enfoques estadísticos.

Understanding inferential statistics and probabilistic models improves machine learning model development. Data distribution, variance, and noise inform algorithm selection and tuning. Statistical tests and confidence intervals improve feature engineering and model complexity decisions by assessing model improvements and feature importance. In regularisation techniques, statistical concepts balance bias-variance trade-offs to optimise model performance and prevent overfitting. Statistical methods allow Bayesian uncertainty estimation, improving model predictions. Understanding statistical hypothesis testing ensures model robustness and reliability through cross-validation and A/B testing.

Complex machine learning models lack explainability, making it difficult to understand their predictions. This is a significant hurdle in various cases, such as regulated industries where black box models are simply unacceptable. Shapash is a Python library that helps you understand machine learning models, by offering a powerful interactive dashboard. Furthermore, shapash can also be used to generate reports, hence being a significantly useful tool for data scientists.

Statistics is the fundamental part of ML. It enables better data preprocessing, feature selection, model evaluation, and addressing issues such as overfitting and biases. Statistical concepts also play a crucial role in probability distributions, parameter estimation, and ethical considerations, contributing to more robust and accurate models.