Decision Tree Regression (ML_R_5)
This function splits the data into smaller subsets while at the same time an associated decision rule is used to predict the target variable, built in the form of a tree structure.
The final prediction is made by traversing the tree from the root to a leaf node, where the value of the target variable is stored. It is simple to understand and interpret, but can be prone to overfitting, particularly when the tree is deep and the data is noisy.
It is most suitable for continuous target variables and it's popularly used in many applications.
Sample Request
Build a multiple linear regression model named, "SimpleModel"
Building a Decision Tree Regression model
Decision Tree Regression
POST
https://autogon.ai/api/v1/engine/start
Request Body
project_id*
int
The id
of the current project
block_id*
int
The id
of the current block
function_code*
string
The function code for current block
parent_id*
int
The id
of the previous block
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).
Sample Request
Make predictions with the pre-built model passing an optional test data.
Predicting with Decision Tree Regression
Decision Tree Regression Predict
POST
https://autogon.ai/api/v1/engine/start
Request Body
model_name*
String
Name of previously trained model to be used for prediction
test_data
String
Input data for prediction. Defaults to x_train_url
in StateManagment
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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