Normalization FeatureCloud App

Description

A Normalization FeautureCloud app, allowing to perform different normalization and standardization techniques in a federated manner.

Input

• train.csv containing the local training data (columns: features; rows: samples)
• test.csv containing the local test data

Output

• train_normalized.csv containing the normalized training data
• test_normalized.csv containing the normalized test data
• train.csv containing the original, unnormalized, local training data
• test.csv containing the original, unnormalized, local test data

Workflows

Can be combined with the following apps:

• Pre: Cross Validation, Feature Selection
• Post: Various Analysis apps (e.g. Random Forest, Linear Regression, Logistic Regression, ...)

Config

Use the config file to customize your training. Just upload it together with your training data as config.yml

fc_normalization:
  input:
    train: "train.csv"
    test: "test.csv"
  output:
    pred: "pred.csv"
    proba: "proba.csv"
    test: "test.csv"
  format:
    sep: ","
    label: "Class" # the label column is excluded from the normalization
  split:
    mode: directory # directory if cross validation was used before, else file
    dir: data # data if cross validation app was used before, else .
  normalization: variance