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#feature-engineering

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YPYejun Parkyejunpark3.hashnode.devMay 30 · 13 min read

From Thousands of Features to a Few: A Deep Dive into Dimensionality Reduction

Imagine you're trying to describe a person to a friend. You could list ten thousand physical measurements—exact hair strand thickness, each fingernail length, the curvature of every knuckle—or you cou

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Ddanieljasmedanieljasme.hashnode.devMay 27 · 9 min read

Building a Near-Perfect Claims Classifier: Random Forest vs XGBoost on TikTok Data (Google Advanced DA Course 5)

This is the fourth and final post in my Google Advanced Data Analytics Certificate series on the TikTok claims classification project. Part 1: data inspection. Part 2: EDA and visualization. Part 3: h

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SMScott McMahanaitransformeronline.hashnode.devMay 4 · 2 min read

Why AI Projects Struggle in Production

A lot of AI systems look great during development but fall apart once they hit production. The issue is rarely the model itself. It usually comes down to inconsistencies in how data is handled across

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KEkingsley ebube onohkingsleyonoh.hashnode.devApr 18 · 6 min read

Why I Split Minutes Prediction Into Two Models Instead of One

A single regression model trained on NBA game logs predicts that Joel Embiid will play 11 minutes in a game where he's listed as OUT. The model has never seen a confident zero. Every row in the training data has some minutes played, because the stand...

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HHHenry Hadatabeginnersfriendlytutorials.hashnode.devApr 15 · 26 min read

Cross-Validation, Pipelines & Hyperparameter Tuning with Scikit-learn

When building a machine learning model, it's tempting to train it on your data, check the score, and call it done. But that score is misleading — the model has already "seen" the data it's being evalu

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PGPrajakta Gunjalprajakta-aiml.hashnode.devApr 12 · 14 min read

How I Built an ML Model to Classify Pancreatic Cancer Subtypes from 60,000 Genes

Where and how did it all start Imagine being handed a spreadsheet with 60,000 columns and being told — somewhere in here is the answer to classifying a cancer that kills 9 out of 10 patients within fi

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RLRisingWave Labsrisingwave.comApr 7 · 12 min read

SQL vs. Python for Feature Engineering: When Each Wins

Feature engineering is where models are won and lost. It's also where ML teams waste enormous amounts of time arguing about tooling rather than building. The SQL vs. Python debate surfaces constantly, and most articles resolve it by quietly promoting...

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RLRisingWave Labsrisingwave.comApr 7 · 13 min read

Real-Time Ad Targeting Features: From Impression to Audience Segment in Milliseconds

The bid request lands in your DSP in 100 milliseconds. You have maybe 10ms of that window to pull features, run your bidding model, and return a price. Meanwhile, the audience segment powering your decision was built from yesterday's batch job. That ...

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RLRisingWave Labsrisingwave.comApr 7 · 12 min read

Underwriting in Under a Second: Real-Time Feature Engineering for Lending

Credit decisions used to take days. A borrower submitted a paper application, a loan officer pulled a bureau report, an underwriter reviewed it overnight, and a letter arrived in the mail. Nobody questioned the timeline — it was just how lending work...

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RLRisingWave Labsrisingwave.comApr 7 · 13 min read

Real-Time Features for Account Takeover Detection

Account takeover (ATO) is the fraud vector that breaks most traditional detection systems -- not because it is technically sophisticated, but because it uses entirely valid credentials. The attacker is not forging a card number or synthesizing a fake...

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Trending tags this week

From Thousands of Features to a Few: A Deep Dive into Dimensionality Reduction

Building a Near-Perfect Claims Classifier: Random Forest vs XGBoost on TikTok Data (Google Advanced DA Course 5)

Why AI Projects Struggle in Production

Why I Split Minutes Prediction Into Two Models Instead of One

Cross-Validation, Pipelines & Hyperparameter Tuning with Scikit-learn

How I Built an ML Model to Classify Pancreatic Cancer Subtypes from 60,000 Genes

SQL vs. Python for Feature Engineering: When Each Wins

Real-Time Ad Targeting Features: From Impression to Audience Segment in Milliseconds

Underwriting in Under a Second: Real-Time Feature Engineering for Lending

Real-Time Features for Account Takeover Detection