Artificial Neural Network Classification

ANN classification refers to the use of Artificial Neural Networks (ANNs) for solving classification problems. ANNs are computational models inspired by the biological neural networks in the human brain. They consist of interconnected nodes called artificial neurons or units, organized into layers.

In the context of classification, ANNs are trained to learn the patterns and relationships in a given dataset and make predictions about the class or category of new, unseen data points. The training process involves presenting the network with a set of labeled examples, where each example consists of input features and the corresponding target class. The network adjusts its internal parameters, known as weights and biases, based on the error between its predictions and the true labels.

The most common architecture for classification tasks is the feedforward neural network, where information flows in one direction from the input layer through one or more hidden layers to the output layer. Each neuron in a layer receives inputs from the previous layer, performs a computation using its weights and biases, and passes the result to the next layer. The output layer typically represents the predicted class probabilities or directly predicts the class label.

Method: POST Authorization: API Key

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

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",
      "hidden_units": [100, 100],
      "max_iter": 1000
    }
  },
  "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 score 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
}