Feature Scaling (DP_5)
This functionality normalizes the range of values for different features in the dataset
This process is very important because many machine learning algorithms use the Euclidean distance between two points in their computations, and if the ranges of the features are vastly different, then the algorithm will be sensitive to the feature with the larger range, and may produce unexpected results.
Sample Request
This request scales the feature sets defined in the range of columns for training and testing.
Missing Data
Encode categorical values
POST https://autogon.ai/api/v1/engine/start
Encodes categorical data on specific columns with specified boundaries
Request Body
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
encode*
boolean
specify if variable is encoded
args
object
block arguments
remainder*
applied method to none specified columns; drop drops the unspecified columns for encoding, passthrough ignores unspecified columns
index*
int
column index to apply encoding technique
scaler
String
Type of scaler to use:
'minmax' : transforms data to a 0-1 range.
'standard' : transforms data to have zero mean and unit variance.
'robust' : scaling technique that is less sensitive to outliers in the data.
'maxabs' : scaling technique that scales the data by dividing each feature by its maximum absolute value, preserving the sign of the values while normalizing them.
save_name
name to save processing models with.
load_name
String
name to load processing models with. Used to switch to loading mode
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