Machine learning is one of the most exciting disciplines in the field of computer science. There are many applications for it in our daily lives that we hardly ever think about. Moreover, there are complex machine learning operations one could never fathom without a formal education in machine learning or investing a lot of time in learning autodidactically.
Machine learning uses algorithms to make predictions. Algorithms are decision-making equations that use real-life data to create formulas for predicting outcomes. Different algorithms perform different functions, and they all have strengths and weaknesses. Logistic regression is one of the most popular algorithms in use, and we'll explain some of the reasons for that in this short article.
Efficiency is one of the things we hope for in every innovation. Can you even call something an innovation if it isn't in some way more efficient than what we used before it?
Efficiency is one of the things that data scientists love about the logistic regression algorithm. More complex algorithms often require vastly more "computational horsepower" to function properly. As you can imagine, these high-powered systems cost more and use a lot more energy than normal computers. Logistic regression is an efficient enough machine-learning algorithm to use for nearly any linear application.
Easy to Learn and Implement
Another thing that makes logistic regression so popular is that it's easy to learn and implement. Its ease of use makes it a great learning tool for future data scientists and people in other tech-related college programs.
There are three types of logistic regression models, and they're all easy to learn and use. Binary, multi-nomial, and ordinal are the three types of logistic regression, and they all have different functions. However, one thing all versions of this machine learning algorithm have in common is they're easy to learn and implement.
Data science is one of the most promising career fields now and in the foreseeable future. TIBCO, one of the leaders in the data science field, offers low-cost licenses for versions of their data science software to college students studying computer science. You can learn how to implement logistic regression using TIBCO tools without having to break your piggy bank.
Various Use Cases
As we mentioned earlier in the article, it's nearly impossible to think of all the ways machine learning impacts the world in which we live. Logistic regression machine learning algorithm powers many of the use cases for machine learning in our daily lives.
One of the most common use cases for logistic regression is the use of the ordinal algorithm for rating services. Ordinal algorithms are equations in which the outputs are multi-nomial and are arranged in order, i.e. 1-5.
Another great use case for this algorithm is in commerce. Manufacturing companies often use it to figure out a new product's likelihood of success. The healthcare, marketing, and financial sectors also use logistic regression.
Challenges With Logistic Regression
Like all algorithms, there are flaws with logistic regression. We live in a nonlinear world, and logistic regression is only capable of handling linear equations, meaning it won't work with many of today's systems. It's also highly prone to over-fitting. Moreover, data scientists have to work to identify all the independent variables as it relies heavily on data presentation. Ultimately, there are more powerful algorithms that are capable of delivering more complex predictions.
However, not many machine learning algorithms are as easy to learn and interpret as logistic regression. It's one of the most popular algorithms for many reasons. There are plenty of use cases for it in various industries, from healthcare to financial services. More importantly, it's efficient and doesn't require high-powered supercomputers. As you can see, regression isn't always a negative thing!