Random Forest Regression
Random Forest regression is a machine learning technique that utilizes an ensemble of decision trees to perform regression tasks. It is an extension of the Random Forest algorithm, which is primarily used for classification problems. Random Forest regression is designed to predict continuous numerical values rather than discrete classes.
Method: POST Authorization: API Keyhttps://engine.raccoon-ai.io/api/v1/ml/regression/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" : <"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
}