Measurement is the first step that leads to control and eventually improvement. H. James Harrington In many business applications, the ability to plan ahead is paramount and in a majority of such scenario we use forecasts to help us plan ahead. For eg., If I run a retail store, how many boxes of that shampoo … Continue reading Forecast Error Measures: Understanding them through experiments
We have come a long way in the world of Gradient Boosting. If you have followed the whole series, you should have a much better understanding about the theory and practical aspects of the major algorithms in this space. After a grim walk through the math and theory behind these algorithms, I thought it would … Continue reading The Gradient Boosters VII: Battle of the Boosters
The reign of the Gradient Boosters were almost complete in the land of tabular data. In most real world as well as competitions, there was hardly a solution which did not have at least one model from one of the gradient boosting algorithms. But as the machine learning community matured, and the machine learning applications … Continue reading The Gradient Boosters VI(B): NGBoost
We are at siege. A siege by an unknown enemy. An enemy with which we are befuddled. And unless you were living under a rock for the past couple of months(like Jared Leto), you know what I’m talking about – COVID-19. Whether you turn on the news, or scroll through social media, the majority of … Continue reading Does Imagenet Pretraining work for Chest Radiography Images(COVID-19)?
We are taking a brief detour from the series to understand what Natural Gradient is. The next algorithm we examine in the Gradient Boosting world is NGBoost and to understand it completely, we need to understand what Natural Gradients are. Pre-reads: I would be talking about KL Divergence and if you are unfamiliar with the … Continue reading The Gradient Boosters VI(A): Natural Gradient
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