Random Forest Classification

Random Forest is a popular ensemble machine learning algorithm used for classification tasks. It is based on the concept of decision trees and combines the predictions of multiple decision trees to make a final classification decision. Random Forest is known for its robustness, accuracy, and ability to handle high-dimensional data.

Method: POST Authorization: API Key

https://engine.raccoon-ai.io/api/v1/ml/classification/randomforest

Authorization

Type	Key	Value
API Key	X-Api-Key	rae_######

Request Body

Section	Key	Data Type	Required	Description
train	data	json	true	Data that use to train the model
	features	list	true	Input features (X)
	targets	list	true	Output targets (y)
	config	json	false	Train configurations
predict	data	json	true	Data that need to predicted by the trained model
	config	json	false	Predict configurations

Types

{
    "train"   : {
        "data"    : <json_data>,
        "features": <list>,
        "targets" : <list>,
        "config"  : {
                "std_scale": <boolean>,
                "encoder"  : <"label" | "drop">,
                "val_size" : <float>
            }
    },
    "predict": {
        "data": <json_data>,
        "config": {
            "include_inputs": <boolean>,
            "round": <int>
        }
    }
}

Sample

{
  "train": {
    "data": {
      "R&D Spend": {
        "0": 165349.2,
        "1": 162597.7,
        "2": 153441.51,
        "3": 144372.41,
        "4": 142107.34,
        "5": 131876.9,
        "6": 134615.46,
        "7": 130298.13,
        "8": 120542.52,
        "9": 123334.88
      },
      "Administration": {
        "0": 136897.8,
        "1": 151377.59,
        "2": 101145.55,
        "3": 118671.85,
        "4": 91391.77,
        "5": 99814.71,
        "6": 147198.87,
        "7": 145530.06,
        "8": 148718.95,
        "9": 108679.17
      },
      "Marketing Spend": {
        "0": 471784.1,
        "1": 443898.53,
        "2": 407934.54,
        "3": 383199.62,
        "4": 366168.42,
        "5": 362861.36,
        "6": 127716.82,
        "7": 323876.68,
        "8": 311613.29,
        "9": 304981.62
      },
      "State": {
        "0": "New York",
        "1": "California",
        "2": "Florida",
        "3": "New York",
        "4": "Florida",
        "5": "New York",
        "6": "California",
        "7": "Florida",
        "8": "New York",
        "9": "California"
      },
      "Profit": {
        "0": 192261.83,
        "1": 191792.06,
        "2": 191050.39,
        "3": 182901.99,
        "4": 166187.94,
        "5": 156991.12,
        "6": 156122.51,
        "7": 155752.6,
        "8": 152211.77,
        "9": 149759.96
      }
    },
    "features": ["R&D Spend", "Administration", "Marketing Spend", "Profit"],
    "targets": ["State"],
    "config": {
      "std_scale": true,
      "encoder": "label"
    }
  },
  "predict": {
    "data": {
      "R&D Spend": {
        "0": 165349.2,
        "1": 162597.7
      },
      "Administration": {
        "0": 136897.8,
        "1": 151377.59
      },
      "Marketing Spend": {
        "0": 471784.1,
        "1": 443898.53
      },
      "Profit": {
        "0": 471784.1,
        "1": 443898.53
      }
    },
    "config": {
      "include_inputs": true,
      "round": 2
    }
  }
}

Reponse Body

Key	Data Type	Description
success	boolean	Indicate the success of the request
msg	string	Message indicators
error	string	Error information, only set if success is false
result	json	Result, only set if success is true
score	json	Accuracy scores of the training and testing phases, only set if success is true
generated_ts	float	Generated timestamp

Types

{
    "success": <boolean>,
    "msg": <string>,
    "error": <string>,
    "result": <json>,
    "score": {
        "train": <float>,
        "test": <float>
    },
    "generated_ts": <timestamp>
}

Sample

{
  "success": true,
  "msg": "Model trained and predicted successfully",
  "error": null,
  "result": {
    "R&D Spend": {
      "0": 165349.2,
      "1": 162597.7
    },
    "Administration": {
      "0": 136897.8,
      "1": 151377.59
    },
    "Marketing Spend": {
      "0": 471784.1,
      "1": 443898.53
    },
    "Profit": {
      "0": 190209.72,
      "1": 186863.18
    },
    "State": {
      "0": "New York",
      "1": "California"
    }
  },
  "score": {
    "train": 0.942446542689397,
    "test": 0.9649618042060305
  },
  "saved_in": null,
  "generated_ts": 1685439220.425382
}