Yellowbrick使用笔记7-聚类可视化

聚类模型是试图检测未标记数据中模式的无监督方法。聚类算法主要有两类：聚集聚类将相似的数据点连接在一起，而质心聚类则试图在数据中找到中心或分区。Yellowbrick提供yellowbrick.cluster用于可视化和评估群集行为的模块。目前，我们提供了几种可视化工具来评估质心机制，特别是K均值聚类，帮助我们发现聚类度量中的佳K参数。

代码下载

主要方法如下：

• Elbow Method：根据某个评分函数对聚类进行可视化，在曲线中寻找“Elbow”。
• Silhouette Visualize：在一个模型中可视化每个集群的轮廓分数。
• Intercluster Distance：可视化簇的相对距离和大小。

本文如果数据集下载不下来，查看下面地址，然后放入yellowbrick安装目录\datasets\fixtures文件夹:

{
  "bikeshare": {
    "url": "https://s3.amazonaws.com/ddl-data-lake/yellowbrick/v1.0/bikeshare.zip",
    "signature": "4ed07a929ccbe0171309129e6adda1c4390190385dd6001ba9eecc795a21eef2"
  },
  "hobbies": {
    "url": "https://s3.amazonaws.com/ddl-data-lake/yellowbrick/v1.0/hobbies.zip",
    "signature": "6114e32f46baddf049a18fb05bad3efa98f4e6a0fe87066c94071541cb1e906f"
  },
  "concrete": {
    "url": "https://s3.amazonaws.com/ddl-data-lake/yellowbrick/v1.0/concrete.zip",
    "signature": "5807af2f04e14e407f61e66a4f3daf910361a99bb5052809096b47d3cccdfc0a"
  },
  "credit": {
    "url": "https://s3.amazonaws.com/ddl-data-lake/yellowbrick/v1.0/credit.zip",
    "signature": "2c6f5821c4039d70e901cc079d1404f6f49c3d6815871231c40348a69ae26573"
  },
  "energy": {
    "url": "https://s3.amazonaws.com/ddl-data-lake/yellowbrick/v1.0/energy.zip",
    "signature": "174eca3cd81e888fc416c006de77dbe5f89d643b20319902a0362e2f1972a34e"
  },
  "game": {
    "url": "https://s3.amazonaws.com/ddl-data-lake/yellowbrick/v1.0/game.zip",
    "signature": "ce799d1c55fcf1985a02def4d85672ac86c022f8f7afefbe42b20364fba47d7a"
  },
  "mushroom": {
    "url": "https://s3.amazonaws.com/ddl-data-lake/yellowbrick/v1.0/mushroom.zip",
    "signature": "f79fdbc33b012dabd06a8f3cb3007d244b6aab22d41358b9aeda74417c91f300"
  },
  "occupancy": {
    "url": "https://s3.amazonaws.com/ddl-data-lake/yellowbrick/v1.0/occupancy.zip",
    "signature": "0b390387584586a05f45c7da610fdaaf8922c5954834f323ae349137394e6253"
  },
  "spam": {
    "url": "https://s3.amazonaws.com/ddl-data-lake/yellowbrick/v1.0/spam.zip",
    "signature": "000309ac2b61090a3001de3e262a5f5319708bb42791c62d15a08a2f9f7cb30a"
  },
  "walking": {
    "url": "https://s3.amazonaws.com/ddl-data-lake/yellowbrick/v1.0/walking.zip",
    "signature": "7a36615978bc3bb74a2e9d5de216815621bd37f6a42c65d3fc28b242b4d6e040"
  },
  "nfl": {
    "url": "https://s3.amazonaws.com/ddl-data-lake/yellowbrick/v1.0/nfl.zip",
    "signature": "4989c66818ea18217ee0fe3a59932b963bd65869928c14075a5c50366cb81e1f"
  }
}

# 多行输出
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"