There are several skills in machine learning where one has to learn different concepts to match the current demand for skills. Many sectors like education, finance, health, gaming, and farming, among others, are deploying machine learning technologies in their products to help them discover and compete with the current trends. They use logistic regression to work with other technologies to achieve better results. According to Indeed UK, at least 100 jobs on their platform require one to have logistic regression as a skill set. This guide will enable you to understand what logistic regression is, its types, applications, assumptions, advantages, and disadvantages.
Logistic regression is a machine learning algorithm that uses supervised machine learning to calculate the outcome of a particular event using probability. It works with different types of data variables whose output is a binary(0 or 1, yes or no), and they are normally dichotomous(Having different categories) in nature.
When working with independent variables that affect the outcome. There are several categories of logistic regression. This include:
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Logistic regression assumptions
Before working on any logistic regression, we make several assumptions about the data we use for training. You have to consider all of them for every project you handle. These assumptions are:
Logistic regressions use logistic functions to change linear data into probability. It uses complex functions to provide S-shaped curves whose data ranges typically from 0 and 1. Most of the time, it fails to represent values that are more than 1.
The sigmoid function helps ensure the predicted values get mapped to the probabilities, and the values range from 0 and 1. To calculate, we use the following formula
Most of the probability has to range from 0 and 1. We use the following formulae to calculate the odds, and most of them are positive.
Odds = p/(1-p)
To get rid of negative, we calculate the log odds using the following formulae.
Log odds = ln(p/(1-p))
To summarize the logistic regression, we can use this.
Where z is an equation with a function (σ) and an output (ŷ).
It's a type of formulae you can use to express probability differently. It is also known as the logit function. It helps people understand the ratio of something happening to another thing not going through and also the ratio of something happening to another thing that can go through and happen.
You first have to calculate the standard logistic function using the following formula.
We then find the logit by using the following formula, where p is the probability.
When working with these formulae, you have to understand different bases depending on the value. For instance, e is the standard base for values greater than1, shannon represents base 2, and hartley represents base 10. Most of these bases vary depending on the values taken by the function.
For infinity numbers, we use the following formulae:
You can calculate the odds ratio by subtracting two probabilities using the following formulae.
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There are several types of logistic regressions. Their differences depend on the theory and how they get executed using the yes or no values. It includes:
1. Binary logistic regression
It doesn't depend on the order of categories, and it normally has two outcomes where the variables can only fall in one of the two required categories. The outcome can be in the form of 0s and 1s, True and False, Yes and No. Examples of this regression include detecting spam in health to detect diseases, finance, and sports.
2. Multinomial logistic regression
This category tests the variables in three or more levels of categories. It doesn't follow any order, and you can have more than two outputs. It's applied in fields like politics, transport, sports, text classification etc
3. Ordinal logistic regression
Their response variables work with more than three categories. They usually rely on the order of categories when looking for an output. Examples include categorizing clothes size, i.e., large, small, medium, calculating distances between two houses etc
There are several uses of linear regression. Some of the common applications of linear regression include:
Financial institutions like banks use predictive models to find out the credit score of their customers. The variables should be easy to read and use. They use variables that participate in the data processing procedures to help find out the variables with the best prediction. Logistic regression supports methods like recursive feature elimination to remove bad variables and improve the accuracy of the output.
2. Health and medicine
Many health companies and research groups use logistics regression to identify diseases and other health issues. They use text analysis to check the vectors by extracting the text into sentences and later converting it into 200-dimensional vectors. After extraction, you train the data and models using logistic regression and predict the outcome of the diseases with much accuracy. Some of the common diseases detected include blood tests, oncology diseases, etc.
3. Text editing
In the current technology, many companies use natural language processing. It involves extracting and processing to provide clear texts and help in other activities. Some of the common applications of logistic regression using natural language processing include detecting hate speech, customer support, sorting emails, etc.
Many companies that handle a lot of PDF documents use logistic regression to extract texts using the OCR system. They later change the text into useful, using different tricks like character training. Character training involves the use of logistic regression to change the lines, identify where punctuation starts, the first and last character of a sentence etc
4. Hotel Industry
Most hotel booking sites across the world have deployed different machine learning algorithms to help them with the different functionalities of their sites. They help gauge the customer's behavior and try to recommend to them what they are up to. Logistic regression uses the data given to evaluate how users interact with the site and when to change the user interface. One of the common examples of this application is booking.com.
5. Gaming industry
Most gamers like games with speed and options like in-app purchases that change different aspects of the game, i.e., characters, communication, etc. Logistic regression uses customers' data by analyzing their behavior and recommending games according to how they play. The algorithm normally recommends them using customers who had the same behavior, the type of games customers put in their account profile, or both factors.
Many companies use logistic regression to measure customers' probability of continuing the subscription or canceling it. It is through monitoring the customer’s behavior using probability. It is common in SAAS businesses.
It can predict which candidate the voter will vote for using their age, voting pattern in the past years, place of residence, income, and race, among others. It forces the politicians to employ data scientists to deploy this algorithm to help in finding out how many votes they can manage.
[Related Article: Classifications in Machine Learning]
Some of the disadvantages of using logistic regression include:
Many businesses have embraced linear regression, and there is a high demand for machine learning specialists. You have to learn how to use it and apply it in real-life scenarios. Most of them are easier to use and need time to learn and master. To work better with the models, you have to ensure you use the best training methods for better accuracy.
Many professionals are now using algorithms in different fields to help them discover different uses for their data. They can use them to improve and get more customers, which leads to more conversion. Many data scientists use the principles to implement them in their daily activities. It has good levels of accuracy that make it a good use to add to your skillset.
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Logistic regression is a machine learning algorithm.
Logistic regression works by using the Sigmoid function.
Below are the steps: To implement the Logistic Regression using Python
The probability of dependent variable p(x) ranging between 0 and 1, i.e. 0<p<1, makes Logistic Regression a Classification algorithm being regression.
Classification stands for several algorithms that try to predict a few outcomes, usually called classes and logistic regression (LR) is a classification technique.
Both perform well and have similar functions. In terms of predictive accuracy, logistic regression is usually better; in terms of ease of use, it has the advantage of more comprehensive software support, and the inference is a simple table.