Machine Learning Basics | Chapter 7: Supervised Learning – Logistic Regression by Prakhar Chauhan | Learn Smarter
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Chapter 7: Supervised Learning – Logistic Regression

Logistic Regression is a machine learning algorithm designed for binary classification problems, transforming categorical outcomes into probabilities using the sigmoid function. It distinguishes between regression and classification methods, showcases dataset preparation and model training, and evaluates models' performance through accuracy scores and confusion matrices.

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Sections

  • 7

    Supervised Learning – Logistic Regression

    Logistic regression is a classification algorithm used for binary outcomes, leveraging the sigmoid function to convert predicted values into probabilities.

    Learning Practice

  • 7.1

    What Is Logistic Regression?

    Logistic Regression is a supervised machine learning algorithm used for binary classification tasks.

    Learning Practice

  • 7.2

    Regression Vs Classification

    This section differentiates between regression and classification in machine learning, highlighting their distinct output types and the algorithms associated with each.

    Learning Practice

  • 7.3

    The Sigmoid Function

    The sigmoid function maps predicted values to probabilities, crucial for binary classification in logistic regression.

    Learning Practice

  • 7.4

    Example: Predicting Exam Pass/fail Based On Hours Studied

    Learning

  • 7.5

    Visualize The Data

    This section focuses on visualizing the relationship between the number of hours students studied and their passing status using a scatter plot.

    Learning Practice

  • 7.6

    Train The Logistic Regression Model

    This section covers the process of training a logistic regression model using the Scikit-Learn library, including preparing data, fitting the model, and making predictions.

    Learning Practice

  • 7.7

    Make Predictions

    In this section, we explore how to make predictions using the logistic regression model.

    Learning Practice

  • 7.8

    Evaluate The Model

    In this section, we learn how to evaluate the effectiveness of a logistic regression model using concepts like accuracy and confusion matrix.

    Learning Practice

  • 7.9

    Visualize The Logistic Curve

    This section explores how to visualize the logistic curve in logistic regression, illustrating the relationship between hours studied and the probability of passing an exam.

    Learning Practice

  • 8

    Summary

    This section encapsulates the key concepts and applications of Logistic Regression in supervised learning.

    Learning Practice

  • 8.1

    Concept Description

    This section covers Logistic Regression, a supervised machine learning algorithm for binary classification, and its associated concepts.

    Learning Practice

References

Untitled document (39).pdf

Class Notes

Memorization

What we have learnt

  • Logistic Regression is prim...
  • The sigmoid function plays ...
  • Evaluating the model is ess...

Final Test

Revision Tests