Week 14 - Machine Learning - (Supervised Learning) Classification model using K-Nearest Neighbor (KNN) for iris from Principles of M.L. Python by Microsoft Learning

Liaw Bei Le · June 22, 2021

Learning   Python   Machine Learning
  • Supervised machine learning terminologies
  • Classification model
  • K-Nearest Neighbor (KNN) algorithm

These are my notes taken from Microsoft Learning’s Principles of Machine Learning in Python - Module 1.

What I learnt:

Overview of KNN classification

Method: Use randomly selected cases to first train and then evaluate a k-nearest-neighbor (KNN) machine learning model.
Goal: To predict the type or class of the label / classify cases with unknown labels.

To create and evaluate a KNN machine learning classification model:

  • Load and explore data using visualization to determine if the features separate the classes.
  • Prepare data by normalizing the numeric features and randomly sampling into training and testing subsets.
  • Construct and evaluate the machine learning model.
  • Evaluation performed by statistically, with accuracy metric and visualization.

I have reviewed and went through the K-Nearest Neighbor (KNN) algorithm for iris flowers in this link.

Twitter, Facebook