Data processing (feature engineering) steps for different models

Others 2021-04-01 15:53:03 views: null

Different models have different data requirements. Some steps may or may not be needed.
Generally, simple models such as tree model and LR are used.

Tree model

Due to the power of lgb, there is no need for normalization, data bucketing, category onehot, etc.

After the feature is constructed, the data file can be saved

Simple model

Such as Ridge and LR, you need to perform data bucketing, normalization, and category onehot

Guess you like

Origin blog.csdn.net/qq_40860934/article/details/114287884

Data processing (feature engineering) steps for different models

Machine learning_feature engineering text processing, data feature preprocessing

Kaggle Intermediate-Machine Learning Data Processing and Feature Engineering

General processing steps for importing GAMIT data from engineering network to COSA

Wherein Engineering (three) different types of data processing pipeline

Data and Feature Processing

[Sklearn] Data preprocessing + feature engineering

Feature Engineering - Data Normalization and Standardization

Feature Engineering Using Image Data

Python machine learning (1) The steps of algorithm learning, the application and process of machine learning (acquiring data, feature engineering, model, model evaluation)

Pandas data cleaning and feature processing

Data Preprocessing---Data Cleaning & Feature Engineering

Feature Engineering – Feature Engineering

【Data analysis】—feature engineering, feature design, feature selection, feature evaluation, feature learning

Ocean magnetic data processing steps

ArcGIS processing nc data steps

Data representation and feature engineering of sklearn library

Summary of Data Preprocessing and Feature Engineering - Feature Selection - Embedding and Packaging (5)

feature engineering

ML's FE: Use [data analysis + data processing] algorithm to perform feature engineering processing on a domestic platform Shanghai house price data set [12+1] in June 2020 (the most complete in history, it is recommended to collect)

The importance of data mining feature # features engineering (two) and interpretability summary

Data Mining Algorithms and Practice (19): Feature Engineering/Model Evaluation

Feature Engineering (2) - Data Preprocessing Interval Scaling Method

Zero-ETL, Big Models, and the Future of Data Engineering

Convolutional neural network feature extraction, convolutional neural network processing data

Feature Engineering: Feature Generation, Feature Selection (3)

Full analysis of second-hand car transaction price prediction code (3) feature engineering and missing value processing

Feature engineering knowledge

Feature Engineering of Recommendation System

Recommended

Ranking

Empire cms smart tag calls four first-level recommended articles, starting from the fourth article

Linux environment installation and configuration Elasticsearch7.17

Big Data processing architecture and Lambda Kappa architecture

Explore the top of the AI large model platform - Wenxin Qianfan

Beijing car PK10 lucky airship Guanya size and value of the odd and even tips

W3B x Sui Hacker House｜In-depth understanding of Sui and Move language

Know almost Ko Chan: Chinese what any decent open source software products? (Finishing from my original answer)

Comprehensively improve AD domain security authentication | Zhuyun IDaaS

Android Update Engine Analysis (24) What happened when making the downgrade package?

Spark Architecture and Operating Mechanism (1) - System Architecture

Daily

2024-05-07(34)

2024-05-06(6)

2024-05-05(0)

2024-05-04(18)

2024-05-03(8)

2024-05-02(0)

2024-05-01(4)

2024-04-30(36)

2024-04-29(5)

2024-04-28(12)