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データ科学基礎   ❯   レッスン一覧   ❯   決定木・k 近傍法   ❯   k 近傍法



k 近傍法

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English  ❯


☰     ❮     L2  ·  L3  ·  L4  ·  L5     ❯⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼⎼
レッスン 4

4.1  不純度

4.2  決定木

❯  4.3  k近傍法

4.4  分類モデルの評価

4.5  ルール誘導

⎺⎺⎺⎺⎺⎺⎺⎺⎺⎺⎺⎺⎺⎺⎺⎺⎺⎺⎺⎺⎺⎺⎺⎺⎺⎺⎺⎺

以下の資料もご参考ください。

▎ENGLISH

What Is K-Nearest Neighbor? An ML Algorithm to Classify Data
▸ https://learn.g2.com/k-nearest-neighbor

How Does K-nearest Neighbor Works In Machine Learning Classification Problem?
▸ https://www.analyticssteps.com/blogs/how-does-k-nearest-neighbor-works-machine-learning-classification-problem

Machine Learning Basics with the K-Nearest Neighbors Algorithm
▸ https://towardsdatascience.com/machine-learning-basics-with-the-k-nearest-neighbors-algorithm-6a6e71d01761

▎日 本 語

kNNの説明とその実装
▸ https://qiita.com/oirom/items/22ccb7c0139dce925f43

kNN(k-Nearest Neighbor method)とは?k近傍法を分かりやすく解説!!
▸ https://nisshingeppo.com/ai/knn/

はじパタ全力解説: 第5章 k最近傍法(kNN法)
▸ https://qiita.com/FukuharaYohei/items/0314f73cebb471deb515

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