Elasticsearch：探索 11 种流行的机器学习算法

在Elasticsearch中，可以使用机器学习功能来应用各种流行的机器学习算法。以下是一些示例：

1. 线性回归

POST /machine_learning_example/_train/regression
{
  "analysis_config": {
    "bucket_span": "30m"
  },
  "input": {
    "search_size": 100,
    "time_field_name": "timestamp",
    "target_field_name": "value",
    "filter": {
      "range": {
        "timestamp": {
          "gte": "now-30d/d",
          "lt": "now/d"
        }
      }
    }
  },
  "ml": {
    "job_id": "regression_1"
  },
  "output": {
    "prediction_field_name": "prediction"
  }
}

2. 决策树

POST /machine_learning_example/_train/decision_tree
{
  "analysis_config": {
    "bucket_span": "30m"
  },
  "input": {
    "search_size": 100,
    "time_field_name": "timestamp",
    "target_field_name": "value",
    "filter": {
      "range": {
        "timestamp": {
          "gte": "now-30d/d",
          "lt": "now/d"
        }
      }
    }
  },
  "ml": {
    "job_id": "decision_tree_1"
  },
  "output": {
    "prediction_field_name": "prediction"
  }
}

3. K-means聚类

POST /machine_learning_example/_train/kmeans
{
  "analysis_config": {
    "bucket_span": "30m"
  },
  "input": {
    "search_size": 100,
    "time_field_name": "timestamp",
    "target_field_name": "value",
    "filter": {
      "range": {
        "timestamp": {
          "gte": "now-30d/d",
          "lt": "now/d"
        }
      }
    }
  },
  "ml": {
    "job_id": "kmeans_1"
  },
  "output": {
    "prediction_field_name": "prediction"
  }
}

这些只是示例，实际应用中可能需要根据数据集和问题进行调整。每个算法都有其特定的参数和配置，需要根据具体情况进行调整。