Machine Learning Feature Selection in Python

4.0
별점
50개의 평가
제공자:
Coursera Project Network
3,270명이 이미 등록했습니다.
학습자는 이 안내 프로젝트에서 다음을 수행하게 됩니다.

Demonstrate univariate filtering methods of feature selection such as SelectKBest

Demonstrate wrapper-based feature selection methods such as Recursive Feature Elimination

Demonstrate feature importance estimation, dimensionality reduction, and lasso regularization techniques

Clock2 hours
Intermediate중급
Cloud다운로드 필요 없음
Video분할 화면 동영상
Comment Dots영어
Laptop데스크톱 전용

In this 1-hour long project-based course, you will learn basic principles of feature selection and extraction, and how this can be implemented in Python. Together, we will explore basic Python implementations of Pearson correlation filtering, Select-K-Best knn-based filtering, backward sequential filtering, recursive feature elimination (RFE), estimating feature importance using bagged decision trees, lasso regularization, and reducing dimensionality using Principal Component Analysis (PCA). We will focus on the simplest implementation, usually using Scikit-Learn functions. All of this will be done on Ubuntu Linux, but can be accomplished using any Python I.D.E. on any operating system. We will be using the IDLE development environment to demonstrate several feature selection techniques using the publicly available Pima Diabetes dataset. I would encourage learners to experiment using these techniques not only for feature selection, but hyperparameter tuning as well. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

개발할 기술

Data SciencePython ProgrammingScikit-Learn

단계별 학습

작업 영역이 있는 분할 화면으로 재생되는 동영상에서 강사는 다음을 단계별로 안내합니다.

  1. Defining Terms relating to Feature Selection and Dimensionality Reduction

  2. Introduce Algorithms with Embedded Feature Selection

  3. Demonstrate two Univariate Selection Methods: Pearson Correlation Filtering and SelectKBest f_classif

  4. Demonstrate two Wrapper Methods: Backward Sequential and RFE

  5. Demonstrate Feature Importance Estimation using Bagged Decision Trees

  6. Dimensionality Reduction using Principal Component Analysis

  7. Demonstrate Lasso Regularization

  8. Expanding concepts to hyperparameter optimization and model selection

안내형 프로젝트 진행 방식

작업 영역은 브라우저에 바로 로드되는 클라우드 데스크톱으로, 다운로드할 필요가 없습니다.

분할 화면 동영상에서 강사가 프로젝트를 단계별로 안내해 줍니다.

자주 묻는 질문

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