Random Forest Regression (ML_R_6)
This function builds multiple decision trees and combines their outputs to make a final prediction.
The algorithm randomly selects a subset of features and samples to train each decision tree, making it less prone to overfitting compared to a single decision tree. The final prediction is made by averaging the predictions of all the trees in the forest. It can be used for both continuous and categorical target variables and is often considered to be a robust and accurate algorithm for regression tasks.
Sample Request
Build a multiple linear regression model named, "SimpleModel"
Building a Random Forest Regression model
Random Forest Regression
POST
https://autogon.ai/api/v1/engine/start
Request Body
Name | Type | Description |
---|---|---|
project_id* | int | The |
block_id* | int | The |
function_code* | string | The function code for current block |
parent_id* | int | The |
args* | object | Block arguments |
model_name* | String | Name of the model to be used for prediction. |
random_state | int | Controls the randomness of the estimator (defaults to 0). |
n_estimators | int | The number of trees in the forest (defaults to 100). |
Sample Request
Make predictions with the pre-built model passing an optional test data.
Predicting with Random Forest Regression
Random Forest Regression Predict
POST
https://autogon.ai/api/v1/engine/start
Request Body
Name | Type | Description |
---|---|---|
model_name* | String | Name of previously trained model to be used for prediction |
test_data | String | Input data for prediction. Defaults to |
project_id* | int | ID of the current project |
block_id* | int | ID of the current block |
parent_id* | int | ID of the previous block |
function_code* | String | Function code for the current block |
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