Great 15 minute live performance from the BBC in 1971 open.spotify.com/track/2iYzC…

The album, which also featured contributions from former King Crimson members, was the band's only album with a three-piece line-up and most of the material was improvised. Although it is considered one of the most successful King Crimson albums, it entered the charts far behind at the time of its release, and Fripp disbanded the band before its release due to his thoughts on the music industry and the future of the world. The album's standout track, Starless, was originally written for the previous album, but was included here because it had not yet been completed | Red, 1974 | Personnel: John Wetton, Bill Bruford, Robert Fripp w/David Cross, Mark Charig, Mel Collins, Ian McDonald, Robin Miller

XGBoost is now the go-to number 1 must-have algorithm in my data science toolkit. But for years, I had no clue what I was doing. In 3 minutes, I’ll share 3 months of research (business case included). Let’s go: 1. XGBoost, which stands for Extreme Gradient Boosting, is an advanced implementation of the gradient boosting machine (GBM) algorithm. It was developed to optimize both computational speed and model performance. 2. Gradient Boosting Machine (GBM): GBMs are an ensemble approach that combines multiple weak learners (typically decision trees) to create a strong predictive model. 3. Difference between GBM and Random Forest: Random Forest also uses decision trees. However, the difference is how the trees are developed. GBM sequentially adds weak learners (shorter trees), where each one corrects its predecessor. RF constructs strong learners (large trees) in parallel using Bootstrap Aggregation (Bagging). 4. Performance: XGBoost is an ultra-fast implementation of GBM that includes high-efficiency, scalability, ability to handle sparse data, parallel learning, and regularization to reduce over-fitting. XGBoost tends to be more efficient than Random Forest and traditional GBM, and often provides better performance. This is why I like it so much. 5. Business Use Cases: I use XGBoost in many business cases. Let’s start with the one that made the most impact, a $12,000,000 sales increase. Lead scoring. In 2 years of developing a Lead Scoring Model that made my company $12,000,000, I used a number of different algorithms. I started with the most basic- Logistic Regression for classification probability of a customer purchasing. And over time, improved it with better algorithms, better and more complex features. The final iteration had XGBoost as a key model in the algorithm. 6. Business Use Case (Time Series): I later discovered that I could use XGBoost on time series for forecasting sales demand at the product level. This was a major improvement over less-scalable techniques (ARIMA, ETS) that had to be run iteratively on every product. We had 12,000 products. XGBoost cut training times from 3 days to about 4 minutes. One thing to watch out for is that because it’s tree-based, XGBoost cannot predict beyond the maximum or below the minimum in a dataset. So differencing may be required. === There’s a lot more to learning Data Science for Business than just XGBoost. I’d like to help. I put together a free on-demand workshop that covers the 10 skills that helped me make the transition to Data Scientist: learn.business-science.io/fr… And if you'd like to speed it up, I have a live workshop where I'll share how to use ChatGPT for Data Science: learn.business-science.io/re… If you like this post, please reshare ♻️ it so others can get value.

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Fantastic performance. Just as fresh as it was in 1969. fripp is melting the walls with his solos

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