Course Content
Identifying Spam Emails
Modeling
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.
Swipe to show code editor
- Import the
LogisticRegression
class. - Initialize the model.
- Use the correct method to fit the model.
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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.
Swipe to show code editor
- Import the
LogisticRegression
class. - Initialize the model.
- Use the correct method to fit the model.