Data Split (DP_4)

This functionality splits data into two subsets: a training set and a test set. The training set is used to train a model, while the test set is used to evaluate its performance.

This process of separating data into two sets is a crucial step in the process of developing and evaluating machine learning models. It ensures that the model is able to generalize well to new, unseen data, and it also allows for a more accurate assessment of the model's performance.

This functionality of splitting data into training and test sets is widely used in the field of machine learning and data science.

Sample Request

This request uses the mean strategy to fill in missing values in the second column to the end with the mean values of the X variable.

{
    "project_id": 1,
    "parent_id": 3,
    "block_id": 4,
    "function_code": "DP_4",
    "args": {
        "test_size": 0.3,
        "random_state": 0
    }
}

Splitting Data

Splits data

POST https://autogon.ai/api/v1/engine/start

Splitting data into training and test data.

Request Body

Name	Type	Description
project_id*	int	current project ID
parent_id*	int	parent block ID
block_id*	int	current block ID
function_code*	String	block's function code
args	object	block arguments
test_size	float/int	If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the test split. If int, represents the absolute number of test samples. If None, the value is set to the complement of the train size.
random_state	int	Controls the shuffling applied to the data before applying the split.

200: OK Feature Scaling Successful

{
    "status": "true",
    "message": {
        "id": 4,
        "project": 1,
        "block_id": 4,
        "parent_id": 3,
        "dataset_url": "",
        "x_value_url": "",
        "y_value_url": "",
        "x_train_url": "",
        "y_train_url": "",
        "x_test_url": "",
        "y_test_url": ""
    }
}

Python

// Some code

Node