@sonariwoondeck: They had us taking a zip line to the mountains and I wanted to pass out😭😭 #Heights #Holiday #fyp #Greece

Michael Sonariwo (SOD)

Open In TikTok:

Region: TR

Friday 08 May 2026 11:55:42 GMT

8593

230

4

1

Music

Download

No Watermark .mp4 (6.6MB) No Watermark(HD) .mp4 (6.6MB) Watermark .mp4 (6.86MB) Music .mp3

Comments

Irma Lidia :

im terrified of heights

2026-05-08 12:39:39

2

Manny :

it's the motion that we are scared of not necessarily the height

2026-05-08 12:40:58

1

pedri..tl :

My big boss more blessed for you

2026-05-08 16:49:15

1

To see more videos from user @sonariwoondeck, please go to the Tikwm homepage.

Other Videos

To Help Our Fan Find His Cow, We Built This...

To Help Our Fan Find His Cow, We Built This...

Instant Wrap/Geos 2in1 boleh ngga sih dipakai untuk Front Facing Out/gendong hadap luar? Yuk diskusi dulu, menurut pandangan mom gimana.. besok aku post VT bahasannya ya😍 #instantwrap #gendonganmshape #geos

Instant Wrap/Geos 2in1 boleh ngga sih dipakai untuk Front Facing Out/gendong hadap luar? Yuk diskusi dulu, menurut pandangan mom gimana.. besok aku post VT bahasannya ya😍 #instantwrap #gendonganmshape #geos

Mong em đừng quên ngày tháng ấy đã có người yêu em hơn chính bản thân họ #xh #fypシ #xuhuong #fyp

Mong em đừng quên ngày tháng ấy đã có người yêu em hơn chính bản thân họ #xh #fypシ #xuhuong #fyp

Building an Automated Trading Model (Part 3) Part 3 of building my automated trading model is all about deciding how the model actually learns from the data. I ended up using something called a HistGradientBoostingClassifier, which is designed to handle messy, nonlinear patterns like the ones you see in the stock market. Instead of trying to learn everything at once, the model builds a sequence of small decision trees, where each new tree focuses on correcting the mistakes made by the previous ones. Over time, those small corrections stack together and create a much stronger overall prediction. This works especially well for market data because relationships between signals are not simple, and different features matter in different conditions. The “hist” part of the model comes from grouping similar values into bins, which makes the training process faster and more efficient without losing important information. By the end, the model produces a score that gets converted into a probability, showing how likely it thinks a meaningful move is. This is important because instead of just guessing, the model is learning patterns, refining its mistakes, and turning that into a confidence-based prediction. Stay tuned for part 4/10! #machinelearning #stockmarket #datascience #datascientist #STEM

Building an Automated Trading Model (Part 3) Part 3 of building my automated trading model is all about deciding how the model actually learns from the data. I ended up using something called a HistGradientBoostingClassifier, which is designed to handle messy, nonlinear patterns like the ones you see in the stock market. Instead of trying to learn everything at once, the model builds a sequence of small decision trees, where each new tree focuses on correcting the mistakes made by the previous ones. Over time, those small corrections stack together and create a much stronger overall prediction. This works especially well for market data because relationships between signals are not simple, and different features matter in different conditions. The “hist” part of the model comes from grouping similar values into bins, which makes the training process faster and more efficient without losing important information. By the end, the model produces a score that gets converted into a probability, showing how likely it thinks a meaningful move is. This is important because instead of just guessing, the model is learning patterns, refining its mistakes, and turning that into a confidence-based prediction. Stay tuned for part 4/10! #machinelearning #stockmarket #datascience #datascientist #STEM

About

Robot
API

Legal

Privacy Policy