Logistic Regression.pptx
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This document provides an introduction to logistic regression, including an overview of its key concepts and applications. Logistic regression can be used for classification problems where the dependent variable is binary. It uses a logistic function to model the probabilities of different outcomes. The loss function for logistic regression is log loss, as it aims to model binary classification probabilities between 0 and 1. Real-world examples of logistic regression include predicting customer purchases, detecting heart disease, and identifying fraudulent activities.
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The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
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- 2. TABLE OF CONTENTS Introduction to Logistic Regression Understanding Regression Need for Logistic Regression The Mathematics Example of Logistic Regression Recap of Loss Function Loss Function in Logistic Regression Achieving Classification from Regression Advantages and Disadvantages of Logistic Regression Real World Examples of Logistic Regression
- 3. 1. It is an example of supervised learning algorithm. 2. It has the unique property of working as a ‘Regression’ as well as a ‘Classification’ algorithm. 3. It is used to predict data sets with or situations which involve calculation of probabilities. 4. It is used for general binary classification. INTRODUCTION TO LOGISTIC REGRESSION
- 4. LOGISTIC ‘REGRESSION’ Why is it called Logistic ‘REGRESSION’ ? a technique for investigating the relationship between independent variables or features and a dependent variable or outcome
- 5. UNDERSTANDING REGRESSION WITH INSECT CHIRPING
- 6. Linear Regression This is an ideal situation for a Linear Regression Model.
- 7. WHEN LINEAR REGRESSION IS NOT ENOUGH A Dataset not suited to Linear Regression. Attempting to solve via Linear Regression.
- 8. WHY LOGISTIC REGRESSION? 1. This is a curve that fits the given data more accurately. 2. There are a few things to note here which differ from Linear Regression Data – a) The range of the data is in between 0 and 1. b) All labels are either 0 or (TRUE or FALSE) 3. This is very similar to a Probabilistic Approach with binary outcomes.
- 9. The Math - Sigmoid Function (The Logistic Function) 1. A sigmoid function is a bounded, differentiable, real function that is defined for all real input values and has a non-negative derivative at each point and exactly one inflection point. A sigmoid "function" and a sigmoid "curve" refer to the same object. a) bounded – implies the value is bound from 0 to 1 or any other ‘x to y’ b) differentiable – it is continuous throughout. c) non-negative derivative – function is only increasing ( as slope always positive) d) one inflection point – there exists only one point post which the graph shows rapid increase
- 10. Note that z is also referred to as the log-odds because the inverse of the sigmoid states that z can be defined as the log of the probability of the 1 label (e.g., "dog barks") divided by the probability of the 0 label (e.g., "dog doesn't bark"): WHY THE SIGMOID FUNCTION? 1. Coming back to the original problem, a model was required to help predict a dataset that could not be fit into Linear Regression. 2. Seeing the data; we require a bounded model whose values should lie between 0 and 1 only.
- 11. USING THE SIGMOID FN FOR LOGISTIC REGRESSION
- 12. EXAMPLE OF LOGISTIC REGRESSION
- 13. RECAP OF LOSS FUNCTIONS The Arrows represent the Respective Losses. HIGH LOSS LOW LOSS The commonly used Loss Function for Linear Regression is the Mean Squared Loss Function.
- 14. LOSS FUNCTION IN LOGISTIC REGRESSION The loss function of Logistic Regression is known as the LOG LOSS
- 15. HOW TO CLASSIFY FROM REGRESSION? To convert the regression output into a Classification Output, we must define a Classification Threshold. If one half of the regression output is one class, this is the value beyond which the other class starts.
- 17. REAL WORLD EXAMPLES The First Tennessee Bank in assosciation with IBM’s SPSS (Statistical Package for the Social Sciences), also known as IBM SPSS Statistics achieved increases upto 600% in cross-sale campaigns using strategies developed through Logistic Regression Models.
- 18. REAL WORLD EXAMPLES Multiple models have been developed for Heart Disease Prediction Using Logistic Regression. This is seen as a simple classification problem of whether a person is more prone to having a heart disease based on the medical records (which are excellent datasets).
- 19. REAL WORLD EXAMPLES Fraud detection: Logistic regression models can help teams identify data anomalies, which are predictive of fraud. Certain behaviors or characteristics may have a higher association with fraudulent activities, which is particularly helpful to banking and other financial institutions in protecting their clients.