@giadinhceohuyquoc: Khi phát hiện ngoại tình… #vochong #ngoaitinh #danong #giadinh #tinhyeu

Gia đình CEO Huy - Quốc

Open In TikTok:

Region: VN

Saturday 28 March 2026 12:10:09 GMT

30213

1224

21

67

Music

Download

No Watermark .mp4 (3.22MB) No Watermark(HD) .mp4 (3.05MB) Watermark .mp4 (3.22MB) Music .mp3

Comments

Hà Xuyên :

hay quá a

2026-05-07 05:27:45

0

huệ sa ngọc tô :

chuẩn

2026-05-09 12:19:08

0

Nguyễn Loan :

2026-03-28 14:25:32

1

M50 mặc gì??? 😜 😜 😜 :

Ừm thì la nv

2026-04-14 04:13:01

0

Tâm An Nhiên :

Quả đúng y như bạn nói 👍

2026-04-14 02:34:51

1

L ặ N g🔒 :

chuan anh ạ

2026-04-12 09:50:39

0

🍀Trần Trang🍀 :

Hay

2026-04-15 01:32:08

0

H Lin Đa Mlô Duôn Du :

Thật sự

2026-04-08 02:13:34

0

🖤 Em 1992🖤 :

Có đúng ko cả nhà

2026-04-01 14:03:07

0

Quymẹ chi tương :

Đúng thật mình chỉ cho hai lựa chọn 1 là chọn nó hai là chọn gd tao còn cho cơ hội để sửa lại

2026-04-28 12:57:19

0

Phúc ơi my nè :

❤️❤️❤️

2026-03-28 12:38:39

1

🦋𝓣𝓱𝓪𝓷𝓱 𝓗𝓸𝓪̀🏹1988🍀 :

👍🏻

2026-03-28 14:53:15

0

Một Đời An Yên :

👍👍👍👍👍

2026-04-07 17:00:43

0

Liên Lưu :

❤️❤️❤️

2026-04-16 16:07:27

0

Một Đời An Yên :

👍👍👍👍👍👍👍👍

2026-04-07 17:00:47

0

Lishra Suara :

👍

2026-03-30 07:50:03

0

Vndkhoavinhomes@ :

@MY LAN APARTMENT

2026-03-31 12:06:50

0

Mỹ Nga (đất Phú trời Yên) :

❤️

2026-04-11 05:14:24

0

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

Other Videos

Welcome to Brixton😎🔥 #footballtiktok #soccertiktok #soccerskills #footballskills #futebol

Welcome to Brixton😎🔥 #footballtiktok #soccertiktok #soccerskills #footballskills #futebol

$Alex leaned back, scanning the email: “Churn is up 15%. Build a model to predict who’s leaving—by next week.” She smiled and waved her junior teammate Ben over. “You’re about to see how an analyst’s brain really works.” Ben sat down eagerly. “I’ve been studying XGBoost and deep learning. Should we start with those?” “Maybe,” Alex said. “But the algorithm is the very last thing you choose. Watch.” She turned to the whiteboard. “Step one: What’s the real question? Marketing doesn’t just want a list; they want to intervene with offers. So we need a model that tells us who is at risk and why. That means interpretability is non-negotiable. No black boxes.” She pulled up the data. “Step two: What does the data ask for? We’ve got 8,000 customers, about 20 features—tenure, spend, support tickets, contract type. It’s tabular, mostly numeric, and the target is binary: churned or not. This is a classic binary classification problem. The algorithm family narrows instantly to logistic regression, decision trees, random forests, and yes, XGBoost. But which one?” Ben looked confused. “That’s still a lot.” Alex drew a mental checklist on the board. “I rank the business constraints. First: Interpretability. Marketing needs to say, ‘This customer’s risk spiked because their support tickets tripled on a monthly plan.’ A neural net can’t give that sentence. Logistic regression gives clear coefficients. A single decision tree draws a literal flowchart. Random forest can show overall feature importance but can’t explain a single prediction easily. So the need for per-customer explanation pushes us toward simpler models.” “Second: Data size. Only 8,000 rows. Fancy models like deep learning or huge gradient boosting ensembles will likely overfit. Logistic regression and small trees generalize beautifully on small data. Third: Speed. They need this next week. I can train logistic regression in seconds, embed a simple formula in their dashboard, and move on. No hyperparameter drama.” “But what about accuracy?” Ben asked. “Accuracy is a trade, not a trophy,” Alex replied. “With small data, a well-tuned simple model often matches a complex one. Even if XGBoost squeezes out an extra 1%, the business will reject it if they can’t trust the why. Trust beats a fraction of a percent every time.” She circled her choices. “So my priority test plan is: Logistic regression first—the honest baseline. If it clearly underfits, I’ll try a tiny random forest and use partial dependence plots to explain non-linear patterns. If we had millions of customers, I’d jump straight to LightGBM. But here, simplicity is our superpower.” Alex grinned. “The secret: We don’t marry an algorithm. We court a few, knowing what each brings to the date. The question shapes the dance floor, the data sets the tempo, and the business constraints are the music you dance to.” A week later, her logistic model identified 85% of churners with crystal-clear reasons. Marketing built a campaign immediately. Ben later tested XGBoost—it gained 0.5% accuracy but delivered explanations so messy that marketing wouldn’t use it. “Choosing an algorithm is just a chain of simple questions,” Alex said. “What’s the real goal? What does the data look like? Who needs to understand it? How fast must it work? Answer those, and the right algorithm reveals itself—long before you open your code editor.” Ben smiled. He’d learned that the real magic happens before you ever type import sklearn. #data #dataengineer #pipeline #model #dataanalytics$
Alex leaned back, scanning the email: “Churn is up 15%. Build a model to predict who’s leaving—by next week.” She smiled and waved her junior teammate Ben over. “You’re about to see how an analyst’s brain really works.” Ben sat down eagerly. “I’ve been studying XGBoost and deep learning. Should we start with those?” “Maybe,” Alex said. “But the algorithm is the very last thing you choose. Watch.” She turned to the whiteboard. “Step one: What’s the real question? Marketing doesn’t just want a list; they want to intervene with offers. So we need a model that tells us who is at risk and why. That means interpretability is non-negotiable. No black boxes.” She pulled up the data. “Step two: What does the data ask for? We’ve got 8,000 customers, about 20 features—tenure, spend, support tickets, contract type. It’s tabular, mostly numeric, and the target is binary: churned or not. This is a classic binary classification problem. The algorithm family narrows instantly to logistic regression, decision trees, random forests, and yes, XGBoost. But which one?” Ben looked confused. “That’s still a lot.” Alex drew a mental checklist on the board. “I rank the business constraints. First: Interpretability. Marketing needs to say, ‘This customer’s risk spiked because their support tickets tripled on a monthly plan.’ A neural net can’t give that sentence. Logistic regression gives clear coefficients. A single decision tree draws a literal flowchart. Random forest can show overall feature importance but can’t explain a single prediction easily. So the need for per-customer explanation pushes us toward simpler models.” “Second: Data size. Only 8,000 rows. Fancy models like deep learning or huge gradient boosting ensembles will likely overfit. Logistic regression and small trees generalize beautifully on small data. Third: Speed. They need this next week. I can train logistic regression in seconds, embed a simple formula in their dashboard, and move on. No hyperparameter drama.” “But what about accuracy?” Ben asked. “Accuracy is a trade, not a trophy,” Alex replied. “With small data, a well-tuned simple model often matches a complex one. Even if XGBoost squeezes out an extra 1%, the business will reject it if they can’t trust the why. Trust beats a fraction of a percent every time.” She circled her choices. “So my priority test plan is: Logistic regression first—the honest baseline. If it clearly underfits, I’ll try a tiny random forest and use partial dependence plots to explain non-linear patterns. If we had millions of customers, I’d jump straight to LightGBM. But here, simplicity is our superpower.” Alex grinned. “The secret: We don’t marry an algorithm. We court a few, knowing what each brings to the date. The question shapes the dance floor, the data sets the tempo, and the business constraints are the music you dance to.” A week later, her logistic model identified 85% of churners with crystal-clear reasons. Marketing built a campaign immediately. Ben later tested XGBoost—it gained 0.5% accuracy but delivered explanations so messy that marketing wouldn’t use it. “Choosing an algorithm is just a chain of simple questions,” Alex said. “What’s the real goal? What does the data look like? Who needs to understand it? How fast must it work? Answer those, and the right algorithm reveals itself—long before you open your code editor.” Ben smiled. He’d learned that the real magic happens before you ever type import sklearn. #data #dataengineer #pipeline #model #dataanalytics

ملك الرياتسو ⏳🔥 || ( @fiizstore + كود الخصم LOKI ) #bleach #aizen #explore #fyp #تيم_شنايدر

ملك الرياتسو ⏳🔥 || ( @fiizstore + كود الخصم LOKI ) #bleach #aizen #explore #fyp #تيم_شنايدر

#SHINOBU : : mientras todos festejaban tu muerte yo m estaba muriendo contigo #viral #anime #fypシ #kny

#SHINOBU : : mientras todos festejaban tu muerte yo m estaba muriendo contigo #viral #anime #fypシ #kny

#tiktok #fyp #foryoupage #متابعه_ولايك_واكسبلووووووور #صنعاء_تعز_عدن_اب_ذمار_حجة_حضرموت 🇾🇪❤🤍🖤🥰🥰

#tiktok #fyp #foryoupage #متابعه_ولايك_واكسبلووووووور #صنعاء_تعز_عدن_اب_ذمار_حجة_حضرموت 🇾🇪❤🤍🖤🥰🥰

About

Robot
API

Legal

Privacy Policy