Multiple Linear Regression

Linear regression is a statistical modeling technique used to understand and analyze the relationship between a dependent variable and one or more independent variables. It assumes a linear relationship between the variables, meaning that the dependent variable can be expressed as a linear combination of the independent variables.

Method: POST Authorization: API Key

https://engine.raccoon-ai.io/api/v1/ml/regression/mlr

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"  : <"onehot" | "label" | "drop">,
                "val_size" : <float>
            },
        "hyper_params": <json>
    },
    "predict": {
        "data": <json_data>,
        "config": {
            "include_inputs": <boolean>,
            "round": <int>
        }
    }
}

Hyper Parameters

Parameter	Type	Default	Description
fit_intercept	bool	true	Whether to calculate the intercept for this model. If set to False, no intercept will be used in calculations (i.e. data is expected to be centered).
copy_X	bool	true	If True, X will be copied; else, it may be overwritten.
n_jobs	int	null	The number of jobs to use for the computation. This will only provide speedup in case of sufficiently large problems, that is if firstly n_targets > 1 and secondly X is sparse or if positive is set to True. None means 1
positive	bool	false	When set to True, forces the coefficients to be positive. This option is only supported for dense arrays.

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", "State"],
    "targets": ["Profit"],
    "config": {
      "std_scale": true,
      "encoder": "onehot"
    }
  },
  "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
      },
      "State": {
        "0": "New York",
        "1": "California"
      }
    },
    "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	r2_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>,
        "validation": <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
    },
    "State": {
      "0": "New York",
      "1": "California"
    },
    "Profit": {
      "0": 190209.72,
      "1": 186863.18
    }
  },
  "score": {
    "train": 0.942446542689397,
    "validation": 0.9649618042060305
  },
  "saved_in": null,
  "generated_ts": 1685439220.425382
}