Grid Search (ML_GRID)
This function exhaustively searches for the optimal combination of hyperparameter values for a machine learning model.
Grid search is a hyperparameter tuning technique used to find the optimal combination of hyperparameter values for a machine learning model. It works by systematically searching through a predefined grid of hyperparameter values, evaluating the model's performance using cross-validation at each point in the grid. Grid search helps to identify the hyperparameter configuration that yields the best performance, enhancing the model's accuracy and generalization ability.
Sample Request
This request is performing a grid search for hyperparameter tuning on the "RandomForest" model. It searches for the best combination of hyperparameters "n_estimators"
, "random_state"
, and "criterion"
by evaluating the model's performance with different values provided in the "param_grid."
Grid Search
POST
https://autogon.ai/api/v1/engine/start
Request Body
Name | Type | Description |
---|---|---|
project_id* | int | ID of the current project |
parent_id* | int | ID of the previous block |
block_id* | int | ID of the current block |
function_code* | String | Function code for the current block |
model_name* | String | Name of the pre-trained model to be used for analysis |
param_grid | object | set of hyperparameter values that the grid search will exhaustively explore to find the optimal combination of hyperparameters |
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