Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.
Modeling | Identifying Spam Emails
Identifying Spam Emails
course content

Course Content

Identifying Spam Emails

bookModeling

We will explore a straightforward model known as Logistic Regression, which is a supervised machine learning algorithm designed for classification problems.

It is particularly useful for predicting binary outcomes (1 / 0, Yes / No, True / False) based on a set of independent variables. The algorithm constructs a model that calculates a probability for each potential outcome and makes predictions based on which outcome is most likely.

The model employs a logistic function to map input variables to probabilities that range between 0 and 1. While primarily used for binary classification, Logistic Regression can also be adapted for multi-class classification through the training of multiple binary classifiers and combining their outcomes. This method is widely utilized in various fields, including medical research, marketing, and social sciences.

Task
test

Swipe to show code editor

  1. Import the LogisticRegression class.
  2. Initialize the model.
  3. Use the correct method to fit the model.

Mark tasks as Completed
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

We will explore a straightforward model known as Logistic Regression, which is a supervised machine learning algorithm designed for classification problems.

It is particularly useful for predicting binary outcomes (1 / 0, Yes / No, True / False) based on a set of independent variables. The algorithm constructs a model that calculates a probability for each potential outcome and makes predictions based on which outcome is most likely.

The model employs a logistic function to map input variables to probabilities that range between 0 and 1. While primarily used for binary classification, Logistic Regression can also be adapted for multi-class classification through the training of multiple binary classifiers and combining their outcomes. This method is widely utilized in various fields, including medical research, marketing, and social sciences.

Task
test

Swipe to show code editor

  1. Import the LogisticRegression class.
  2. Initialize the model.
  3. Use the correct method to fit the model.

Mark tasks as Completed
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 1. Chapter 10
AVAILABLE TO ULTIMATE ONLY
We're sorry to hear that something went wrong. What happened?
some-alt