Logloss dataset for GBDTE
Logloss dataset for GBDTE It seems that I repeated the post about dataset generation twice, so let's move on. The picture shows lift graphs for 16 features. One can see that the lines are uneven - it's because of randomn
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Logloss dataset for GBDTE It seems that I repeated the post about dataset generation twice, so let's move on. The picture shows lift graphs for 16 features. One can see that the lines are uneven - it's because of randomn
GBDTE: LogLoss dataset My approach with the synthetic MSE dataset was successful. I created it, and all experiments gave me quite expected results. The next frontier is a synthetic dataset for testing the logloss functio
TFWR. Labyrinth Let’s think about the labyrinth problem in The Farmer Was Replaced game. First of all, let’s state it. We have an 𝑛×𝑛 labyrinth, where n = get_world_size(). In position measure() there is a treasure. Our
When is a door not a door? I first heard this joke in the 1997 animated movie Anastasia, and it’s stuck with me ever since. In the film it’s treated like one of those classic jokes everyone’s supposed to know. Do you kno
Riddle of the day "What number, when you remove one letter from its spelling, transforms into an even number?" UPD: There are slightly different options for this riddle, like "I’m an odd number. If I lose one letter, I b
In my other channel I published post about physical exposition which my father and I found in 1988. Quite an exclusive [material](<https://t.me/alina_yerevan_js/122>) . In Russian.
GBDTE log‑loss dataset In this post I want to solemnly declare: I'm not a mathematician. My friends who are, I'm totally sure, would solve this problem without effort using Bayes' formalism. I can only wave my hands. So,
Big MSE dataset. Depth of trees. There are 128 groups in the Big dataset. And to distinguish them perfectly, it's necessary to use exactly 7 binary features. Why is it so? Because when we add one more level to a decision
Big MSE dataset for GBDT In the previous post I demonstrated a small dataset that shows how GBDT works. Now I want to present you quite a big one. There are 10 000 points in it. I wasn't happy with the quality of the tex
Liquid Nitrogen Station An attentive reader of one of my previous posts about the diamond plate in an electron microscope could notice a repetitive note: let's pour liquid nitrogen here, let's pour liquid nitrogen there.
Torus navigation Problem statement: issue control commands on torus map to navigate bot to a given point. Essence * pythonic (x2-x1) % n gives eastward toroidal distance * easier to think in "direction first" terms * go_
Dawn of the day It dawned on me today that Harlequin ↔️ Harley Quinn. An example of paronomasia (a pun)—more specifically, it works like an eggcorn/mondegreen for a proper name
Some time ago my friend, who worked with native English speakers, told me that when they worked with a colleague on a piece of technical documentation, the word “she” suddenly appeared referring to the “user.” They discu
Eggcorn of the day Bone apple tea → bon appétit
Hello everyone! I really appreciate that all of you have signed up for my channel. It's kinda touching, and I feel a little bit awkward just writing about GBDT and TFWR (but what else could I write about…). So, I’d like
TFWR. Navigation. In order to navigate the drone, there are get_pos_x(), get_pos_y(), and move() functions. move() takes East (x + 1), North (y + 1), West (x − 1), and South (y − 1) commands. get_world_size() is useful t
Memories Awakened by “The Farmer Was Replaced” It seems like a very trivial question: “What should a drone do if it is on the lower (South) edge of the field and it has a move(South) command to perform?” But, strangely,
The Farmer Was Replaced In one of ML channels I'm reading, I found a reference to the game "The Farmer Was Replaced". Here you are to write programs on a language which is quite similar to Python, but is not quite Python
Everybody wants to be like ewe [Track on youtube ](<https://youtu.be/LjQ0hO89xrw?si=8Ubzxl9JjMLISk9C&t=17>) Just a small reminder: 🐑Homophones - "you" and "ewe", same pronunciation but different meanings 🐑Homographs - sa