Episode

Logistic Regression for classification of data (Part 16 of 17) | Machine Learning for Beginners

with Bea Stollnitz

Machine Learning for Beginners

In this video, Bea Stollnitz, a Principal Cloud Advocate at Microsoft, guides you through training a logistic regression model using the pumpkin data we cleaned and transformed in the previous video.

What you'll learn:

  • How to divide data into input features and labels
  • How to create a logistic regression model and train it using our 🎃 data
  • How to analyze the predictions using accuracy, precision, recall and F1 score

Join Bea as she unravels the fascinating world of logistic regression, and learn how it can be utilized in classification problems. This video is perfect for those who want to expand their understanding of regression techniques and enhance their machine learning skill set.

Stay tuned for the next video in this series, you'll learn one other method that helps you analyze the quality of your model: ROC curves. See you there!

Chapters

  • 00:00 - Introduction
  • 00:16 - The notebook we are using
  • 00:43 - Divide the data into input features and label
  • 00:57 - Train/test splot
  • 01:08 - Create a logistic regression class using SciKit Learn
  • 01:30 - Analyze the results of the regression
  • 01:45 - What is a confusion matrix?
  • 02:35 - Calculate accuracy
  • 02:52 - Precision, recall, and F1 score
  • 03:55 - Macro average and weighted average
  • This course is based on the free, open-source, 26-lesson ML For Beginners curriculum from Microsoft.
  • The Jupyter Notebook to follow along with this lesson is available!

Connect

Azure Machine Learning
Python

Have feedback? Submit an issue here.