Gradient Boosting Classification

Gradient boosting classification is a machine learning algorithm that uses a series of weak learners to create a strong classifier. The weak learners are trained sequentially, with each learner being trained to correct the errors made by the previous learners. This process is repeated until the desired level of accuracy is achieved.

Gradient boosting classification is a powerful algorithm that can be used to solve a variety of classification problems. It is particularly well-suited for problems where the data is noisy or imbalanced. However, it can be computationally expensive to train, and it can be prone to overfitting.

Method: POST Authorization: API Key

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

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,
    "validation": 0.9649618042060305
  },
  "saved_in": null,
  "generated_ts": 1685439220.425382
}