I’m a new to python as well as machine learning. I’m trying to use logistic regression for federated learning program for multiclass labels (sitting, sittingdown, standing, standingup, walking) for UCI HAR Dataset. it’s working for binary classification but for multiclassification when I’m trying to find out precision and recall, it is throwing the following error:
ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].
Can anyone please suggest me how to proceed with it? I have tried to classify 6 labels by LG classified (multi-classes) with FL instead of using binary classification.
code:

  
      github.com
  
  
    datafleets/horizontal-federated-learning-blog/blob/master/horizontal-fl.ipynb 16
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Federated averaging with non-IID data in multiple silos\n",
    "\n",
    "This notebook explores the robustness of model/gradient averaging with respect to varying degrees of non-iid'ness and different data partitioning schemes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "import math\n",
    "import random\n",


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