Logistic Regression

Logistic regression is a statistical technique used for binary classification problems. It is commonly used when the dependent variable, or the target variable, is binary or categorical in nature, and the goal is to predict the probability of an instance belonging to a particular class.

Unlike linear regression, which predicts continuous numerical values, logistic regression models the relationship between the input features and the probability of the target variable belonging to a specific class. The predicted probability is then mapped to a binary outcome using a threshold value.

Method: POST Authorization: API Key

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

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>
            }
    },
    "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", "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>,
        "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
    },
    "State": {
      "0": "New York",
      "1": "California"
    },
    "Profit": {
      "0": 190209.72,
      "1": 186863.18
    }
  },
  "score": {
    "train": 0.942446542689397,
    "test": 0.9649618042060305
  },
  "saved_in": null,
  "generated_ts": 1685439220.425382
}