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◈ EN | JP ◆

Lesson 14    ❮    Lesson List    ❮    Top Page


◆  Intro to Feature Selection


◈  Statistics-based Methods


◆  Dimensionality Reduction-based


◆  Information Theory-based


◆  Wrapper-type Methods


◆  Genetic Algorithm-based


⟐  Implementation

See also the following links:

▎ENGLISH

Feature Selection using Statistical Tests
▸ https://www.analyticsvidhya.com/blog/2021/06/feature-selection-using-statistical-tests/

Feature Selection in Machine Learning: Correlation Matrix
▸ https://medium.com/geekculture/feature-selection-in-machine-learning-correlation-matrix-univariate-testing-rfecv-1186168fac12

NLP — Feature Selection using TF-IDF
▸ https://medium.com/analytics-vidhya/nlp-feature-selection-using-tf-idf-db2f9eb484fb


▎日 本 語

特徴量の確認と選択、相関係数で選択する
▸ https://panda-clip.com/select-variable-1/

Weight by Deviation - Rapidminer (JP)
▸ https://support.rapidminer.jp/rapidminer-studio/operators-list/9.5/modeling/feature_weights/weight_by_deviation

Weight by Correlation - Rapidminer (JP)
▸ https://support.rapidminer.jp/rapidminer-studio/operators-list/9.5/modeling/feature_weights/weight_by_correlation

©2023. All rights reserved.  Samy Baladram,
Graduate Program in Data Science - GSIS - Tohoku University
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