This post aims to explore decision trees for the NOVA Deep Learning Meetup. It is based on chapter 8 of An Introduction to Statistical Learning with Applications in R by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani. It also discusses methods to improve decision tree performance, such as bagging, random forest, and boosting. There are two posts with the same material, one in R and one in Python.

This post aims to explore decision trees for the NOVA Deep Learning Meetup. It is based on chapter 8 of An Introduction to Statistical Learning with Applications in R by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani. It also discusses methods to improve decision tree performance, such as bagging, random forest, and boosting. There are two posts with the same material, one in R and one in Python.

This post is about SQLAlchemy and working with Object Relational Mappers (ORMs).

This notebook contains my cheat sheet for working with PostgreSQL databases.

Bayes’ Theorem is a fundamental concept in probability theory and statistics, offering a powerful framework for updating our beliefs in light of new evidence. This theorem is particularly useful in fields like machine learning, medical diagnostics, and even everyday decision-making. But what happens when we lack certain probabilities, specifically P(B)? This post talks about how Bayes’ Theorem works and explores strategies to apply it even without direct knowledge of P(B).

This post contains some of my favorite tips and tricks for working with VSCode. For even more, I recommend VSCode’s own tips and tricks page.