Logistic Regression and Machine Learning: First Steps, Algorithms, and Main Challenges

August 5, 2021
Logistic Regression and Machine Learning: First Steps, Algorithms, and Main Challenges

Data analytics is a complex field that consists of different pillars connected with each other. According to Statista, the market survey report showed that the total amount of data being consumed globally was forecasted to increase rapidly to 64.2 zettabytes in 2020, 79 zettabytes in 2021, and over 180 zettabytes in 2025.

Data analysis is built on the 3 pillars: the fundamentals of logistic regression, classification algorithms, and modeling. To fully understand data analysis, you need to understand the main concepts in each pillar as well as how the pillars work together. Logistic regression is a type of regression analysis that is used to find the relationships between a dependent variable and either one or a series of independent variables, with the goal of predicting a binary outcome based on a set of independent variables. Sounds complicated? Keep reading our blog post to learn more about the ins and outs of logistic regression.

What is logistic regression and its examples

So what is a logistic regression? Before answering this question, let’s discuss predicting binary outcomes. In simple words, a binary outcome includes only two possible scenarios: either the event happens (1) or it does not happen (0). These outcomes are influenced by independent variables. Independent variables come in three categories:

  • Continuous - data that can take any value, such as height, weight, temperature and length; continuous variables often change over time.
  • Discrete, ordinal - data you can put into some order on a scale
  • Discrete, nominal - data that belongs to certain named groups (for example, nationality, gender, race, hair color, etc. of a group of people)

Logistic regression is a classification algorithm used to predict a binary outcome based on a set of independent variables. Here is an example: let’s say you need to detect if a certain email contains spam. In such a case, if the email is spam, we label it 1; if it is not spam, we label it 0. In order to identify it, we can extract data such as the sender of the email, number of typos in the email, and frequency of words/phrases like “offer,” “prize,” “free,” etc.

A logistic classifier is trained by a vector that gives a score in the range 0 to 1. If the score is more than 0.5, the email is labeled as spam. If the score is less than or equal to 0.5, the email is not labeled as spam. That’s how logistic regression for binary classification looks. This data is then fit into a linear regression model, which predicts the target categorical dependent variable.

Logistic regression classification algorithm

How to classify logistic regression? Logistic regression becomes a classification technique only when a decision threshold exists. While linear regression models can be successfully used for regression, it’s not efficient for classification.

Image by Proxet. Logistic Regression Classification Algorithm
Logistic Regression Classification Algorithm

Linear models are not good for classification because linear models do not include output probabilities and treats classes as numbers (0 and 1) with a t hyperplane that minimizes the distances between the points and the hyperplane. In other words, linear models interpolate between the points so it’s not possible to interpret it as probabilities. Classifications cannot be distinguished from one another because the predicted outcome is not a probability, but a linear interpolation between points.

For example, let’s say you have a lot of data about different houses and want to predict the price of a certain one. In a regression task, the model will analyze such features as location, the number of rooms, square footage of the home and plot of land, house age, and try to predict a numerical value—the price of the house.

In a classification task, the outputs would fall into one of a few different categories and a classification algorithm will label the example with one of the following categories:

  • Much lower than expected price
  • Lower than expected price
  • Approximately expected price
  • Higher than expected price
  • Much higher than expected price

Logistic regression problems and challenges

Logistic regression analysis has a range of disadvantages you need to take into account before choosing this type of data analysis

  • If the number of observations is less than the number of features, logistic regression may lead to overfitting
  • It may lead to linear boundaries
  • The assumption of linearity between the dependent variable and the independent variables limits the capacity of logistic regression
  • It can only be used to predict discrete functions, because the dependent variable of logistic regression depends on the discrete number set
  • Logistic regression can’t solve nonlinear problems because it has a linear decision surface
  • Logistic regression requires average or no multicollinearity between independent variables
  • It’s better to use more powerful and compact algorithms such as neural networks to obtain complex relationships
  • Logistic regression requires independent variables to be linearly related to the log odds (log(p/(1-p))

Logistic regression algorithm types and when to use them

How to build a logistic regression model? Logistic regression algorithms usually consists of the following types:

  • Linear regression
  • Ridge regression
  • Lasso regression
  • Polynomial regression

One of the most basic types of logistic regression machine learning, linear regression includes a predictor variable and a dependent variable related to each other in a linear fashion.

Linear regression is used when the variables are related linearly, for example, in forecasting the effect of increased advertising spend on sales. However, it’s better not to use this analysis for big data sets, as it is susceptible to outliers.

Ridge regression is used when you have a high correlation between independent variables. It includes a small amount of bias which makes the model less susceptible to overfitting.

Lasso regression reduces the model’s complexity by prohibiting the absolute size of the regression coefficient. This causes the coefficient value to become closer to zero.

Polynomial regression transforms these data points into polynomial features of a given degree using a curved polynomial line. However, you must analyze the curve towards the end to avoid strange and ambiguous results.

Logistic regression modeling in machine learning

What is a logistic model? Logistic regression in data mining is a supervised machine learning classification algorithm. Logistic regression modeling is used in machine learning to:

  • Identify risk factors for diseases and planning preventive measures
  • Classify words as nouns, pronouns, and verbs
  • Forecast applications for predicting rainfall and weather conditions
  • Predict whether voters will vote for a particular candidate or not

The best part of logistic regression in machine learning is that we can include more explanatory (dependent) variables such as dichotomous, ordinal, and continuous variables to model binomial outcomes. It uses binary classification to reach specific outcomes and models the probabilities of default classes.

“In many industries, machine learning and AI is becoming a must-have to stay competitive. It’s critical that companies that want to stay ahead of competitors find an experienced technical partner to guide them through the software development process and identify how data analytics will help them streamline their business and services.”

Vlad Medvedovsky, Founder and CEO at Proxet (ex - Rails Reactor), a software development solutions company

Logistic regression and machine learning first steps and main benefits

  • Logistic regression is one of the most popular machine learning algorithms used for predicting the categorical dependent variable given a set of independent variables.
  • In logistic regression, we fit an "S" shaped logistic function, which predicts two maximum values (0 or 1).
  • The curve from a logistic function can indicate the likelihood of events, such as whether cells are cancerous or not.
  • Logistic regression can detect observations using different types of data and can easily determine the most effective variables used for classification.
“In the early days of machine learning work, most machine learning models were developed on the local machines of data scientists (on laptops, even!) and then models moved or ported once the desired objectives had been reached. However, the emergence of strong cloud-based alternatives provides a way to run machine learning projects from start to finish in a cloud-based environment.”

— a Managing Partner & Principal Analyst at Cognilytics, an AI Focused Research and Advisory firm

Logistic regression modeling

Here are the main types of predictive models that use logistic analysis:

  • Generalized linear model
  • Discrete choice
  • Multinomial logit
  • Mixed logit
  • Probit
  • Multinomial probit
  • Ordered logit

The main difference between logistic regression and linear regression is that logistic regression provides a constant output, while linear regression provides a continuous output. Logistic regression is used when the response variable is categorical and linear regression is used when the response variable is continuous, such as number of hours, height, and weight. Here are examples of how logistic regression modeling is being applied:

  • To predict the possibility of a person being afflicted by a certain disease. For example, ailments like diabetes and heart disease can be predicted based on variables such as age, gender, weight, and genetic factors
  • To predict elections
  • To predict the chances of a customer’s inquiry turning into a sale and a subscription being started or terminated
  • To predict the likelihood that a customer will default on their payments in the banking industry
  • To maximize return on investment (ROI) in marketing campaigns and increase sales in e-commerce
“Machine learning is one of the most promising technologies with the potential to revamp the financial sector today. AI platforms allow banks to automate processes, better understand customers, and advance overall service quality.”

— Taras Kloba, Head of Data Center of Excellence at Intellias.

If you want to leverage data analysis for your next project, don’t hesitate to contact Proxet, a company developing state-of-the-art software solutions for startups, SMBs, and enterprises. Sixty percent of our projects have AI/ML components, including everything from chatbot implementation to image recognition and sentiment analysis. We expertly guide our clients through their digital journey to success.

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