본문 바로가기
장바구니0

Feature Engineering and Selection: A Practical Approach for Predictive Models > 데이터마이닝

도서간략정보

Feature Engineering and Selection: A Practical Approach for Predictive Models
히트도서
판매가격 69,000원
저자 Max Kuhn
도서종류 외국도서
출판사 CRC
발행언어 영어
발행일 2019-9
페이지수 308
ISBN 9781138079229
도서구매안내 온, 온프라인 서점에서 구매 하실 수 있습니다.

구매기능

보조자료 다운
  • 도서 정보

    도서 상세설명

    Table of Contents

    1. Introduction
    A Simple Example
    Important Concepts
    A More Complex Example
    Feature Selection
    An Outline of the Book
    Computing

    2. Illustrative Example: Predicting Risk of Ischemic Stroke
    Splitting
    Preprocessing
    Exploration
    Predictive Modeling Across Sets
    Other Considerations
    Computing

    3. A Review of the Predictive Modeling Process
    Illustrative Example: OkCupid Profile Data
    Measuring Performance
    Data Splitting
    Resampling
    Tuning Parameters and Overfitting
    Model Optimization and Tuning
    Comparing Models Using the Training Set
    Feature Engineering Without Overfitting
    Summary
    Computing

    4. Exploratory Visualizations
    Introduction to the Chicago Train Ridership Data
    Visualizations for Numeric Data: Exploring Train Ridership Data
    Visualizations for Categorical Data: Exploring the OkCupid Data
    Post Modeling Exploratory Visualizations
    Summary
    Computing

    5. Encoding Categorical Predictors
    Creating Dummy Variables for Unordered Categories
    Encoding Predictors with Many Categories
    Approaches for Novel Categories
    Supervised Encoding Methods
    Encodings for Ordered Data
    Creating Features from Text Data
    Factors versus Dummy Variables in Tree-Based Models
    Summary
    Computing

    6. Engineering Numeric Predictors
    Transformations
    Many Transformations
    Many: Many Transformations
    Summary
    Computing

    7. Detecting Interaction Effects
    Guiding Principles in the Search for Interactions
    Practical Considerations
    The Brute-Force Approach to Identifying Predictive Interactions
    Approaches when Complete Enumeration is Practically Impossible
    Other Potentially Useful Tools
    Summary
    Computing

    8. Handling Missing Data
    Understanding the Nature and Severity of Missing Information
    Models that are Resistant to Missing Values
    Deletion of Data
    Encoding Missingness
    Imputation methods
    Special Cases
    Summary
    Computing

    9. Working with Profile Data
    Illustrative Data: Pharmaceutical Manufacturing Monitoring
    What are the Experimental Unit and the Unit of Prediction?
    Reducing Background
    Reducing Other Noise
    Exploiting Correlation
    Impacts of Data Processing on Modeling
    Summary
    Computing

    10. Feature Selection Overview
    Goals of Feature Selection
    Classes of Feature Selection Methodologies
    Effect of Irrelevant Features
    Overfitting to Predictors and External Validation
    A Case Study
    Next Steps
    Computing

    11. Greedy Search Methods
    Illustrative Data: Predicting Parkinson’s Disease
    Simple Filters
    Recursive Feature Elimination
    Stepwise Selection
    Summary
    Computing

    12. Global Search Methods
    Naive Bayes Models
    Simulated Annealing
    Genetic Algorithms
    Test Set Results
    Summary
    Computing
  • 사용후기

    사용후기가 없습니다.

  • 도서문의

    도서문의가 없습니다.

  • 배송/교환정보

    배송정보

    배송 안내 입력전입니다.

    교환/반품

    교환/반품 안내 입력전입니다.

선택하신 도서가 장바구니에 담겼습니다.

계속 둘러보기 장바구니보기
회사소개 개인정보 이용약관
Copyright © 2001-2019 도서출판 홍릉. All Rights Reserved.
상단으로