The Basics of Logistic Regression in Data Science

Data science is a field that is growing by leaps and bounds. A couple decades ago, using a machine learning program to make predictions about datasets was the purview of science fiction. Today, all you need is a computer, some solid programming, and a bit of patience, and you, too, can have your own digital Zoltar.

Okay, it’s not really telling the future, but it does give you the ability to predict some future events, as long as those events fall within the data the system already has. These predictions fall into two categories: linear regression and logistic regression.

Today we’re going to focus on the latter. What is logistic regression in data science, and how can it help data scientists and analysts solve problems?

What Is Logistic Regression?

First, what is logistic regression, and how does it compare to its linear counterpart?

Logistic regression is defined as “a statistical analysis method used to predict a data value based on prior observations of a data set.” It is valuable for predicting the result if it is dichotomous, meaning there are only two possible outcomes.

For example, a logistic regression system could use its data set to predict whether a specific team will win a game, based on their previous performance and the performance of their competitors because there are only two possible outcomes — a win or a loss.

Linear regression, on the other hand, is better suited for continuous output or situations where there are more than two possible outcomes. A traffic prediction model would use linear regression. In these situations, no matter how much data the program has, there are always variables that can throw a wrench in the works.

This isn’t just a case of “go” or “no go,” like you might see with a rocket launch. A company using these predictive models to schedule deliveries will need to be able to compensate for those variables. Linear regression gives them the flexibility to do that.

Logistic Regression in Data Analysis

If you broke down the expression of logistic regression on paper, it would look something like this:

The left side of the equation is called a logit, while the right side represents the odds, or the probability of success vs. failure.

It seems incredibly complicated, but when you break it down and feed it into a machine learning algorithm, it provides a great number of benefits. For one, it’s simple — in relative terms — and doesn’t generate a lot of variance because, at the end, no matter how much data you feed a logistic regression system, there are only going to be two possible outcomes.

In addition to providing you with a “yes” or “no” answer, these systems can also provide the probability for each potential outcome. The downside of logistic regression systems is that they don’t function well if you feed them too many different variables. They will also need an additional translator system to convert non-linear features to linear ones.

Applications of Logistic Regression

The potential applications for logistic regression are nearly limitless. But if you find yourself struggling to picture where you might use this data analysis tool, here are a few examples that might help you.

Organizations can use logistic regression for credit scoring. While this might seem like it’s outside the scope of this particular type of programming because there are multiple credit scores a person might have, when you break it down to ones and zeros, there are usually only two possible outcomes where credit is concerned — approved or denied.

There are a number of potential applications for logistic regression in medicine as well. When it comes to testing for a specific condition, there are usually only two results — “yes” or “no.” Either the patient has the condition, or they don’t. In this case, it may be necessary to program in a third option — a null variable — that trips when there isn’t enough information available for a particular patient to make an accurate diagnostic decision.

Even text editing can benefit from logistic regression. Plug in a dictionary, and let it loose on a piece of text. These programs aren’t going to create masterpieces or even fix editing mistakes because they lack the programming to understand things like context and tone, but they are invaluable for spelling mistakes. Again, this brings us back to the binary of 1’s and 0’s — either a word is spelled correctly, according to the dictionary definition, or it isn’t.

If dichotomous outcomes are the goal of your data analysis system, logistic regression is going to be the best tool in your toolbox.

Breaking Down the Basics

A lot goes into the creation of a machine learning or predictive analysis tool, but understanding the difference between linear and logistic regression can make that process a bit simpler. Start by understanding what the program will do, then choose your regression form appropriately.

Why Your Data Science Team Needs Separate Testing, Validation & Training Sets

Automated testing of machine learning models can help to dramatically reduce the amount of time and effort that your team has to dedicate to debugging them. Not applying these techniques properly, however, could potentially do a great deal of harm if the process is completely unmonitored and doesn’t adhere to a list of best practices. Some tests can be applied to models after the training stage is over while others should be applied directly to test the assumptions that the model is operating under.

Traditionally, it’s proven somewhat difficult to test machine learning models because they’re very complex containers that often play host to a number of learned operations that can’t be clearly decoupled from the underlying software.. Conventional software can be broken up into individual units that each accomplish a specific task. The same can’t be said of ML models, which are often solely the product of training and therefore can’t be decomposed into smaller parts.

Testing and evaluating the data sets that you use for training could be the first step in unraveling this problem.

Monitoring Data Sets in a Training Environment

ML testing is very different from testing application software because anyone performing checks on ML models will find that they’re attempting to test something that is probabilistic as opposed to deterministic. An otherwise perfect model could occasionally make mistakes and still be considered the best possible model that someone could develop. Engineers working on a spreadsheet or database program wouldn’t be able to tolerate even the slightest rounding errors, but it’s at least somewhat acceptable to find the occasional flaw in the output of a program that processes data by way of learned responses. The level of variance will differ somewhat depending on the tasks that a particular model is being trained to accomplish, but it may always be there regardless.

As a result, it’s important to at least examine the initial data that’s being used to train ML models. If this data doesn’t accurately represent the kind of information that a real-world environment would thrust onto a model, then said model couldn’t ever hope to perform adequately when such input is finally given. Decent input specifications will help to ensure that the model comes away with a somewhat accurate representation of natural variability in whatever industry it’s performing a study in.

Pure performance measurements can come from essentially any test set, but data scientists will normally want to specify the hyperparameters of their model to provide a clear metric by which to judge performance while taking said measurements. Those who consistently use one model over another solely for its performance on a particular test set may end up fitting test sets to models to find something that performs exactly as they want it to.

Those who are working with smaller data sets will need to find some way to evaluate them in spite of their diminutive size.

Managing a Smaller Set of Data Safely

Those working with particularly large data sets have typically gone with 60-20-20 or 80-10-10 splits. This has helped to strike a decent balance between the competing needs of reducing potential bias while also ensuring that the simulations run fast enough to be repeated several times over.

Those working with a smaller data set might find that it simply isn’t representative enough, but for whatever reason it isn’t possible to increase the amount of information put into the test set. Cross-validation might be the best option for those who find themselves in this sort of situation. This is normally used by those in the applied ML field to compare and select models since it’s relatively easy to understand.

K-fold cross-validation algorithms are often used to estimate the skill of a particular ML model on new data irrespective of the size of the data in question. No matter what method you decide to try, though, your team needs to keep the concepts of testing and validation data separate when training your ML model. When you square these off in a training data vs. test data free-for-all, the results should come out something like this:

• Test sets are essentially examples that are only ever used to judge the performance of a classifier that’s been completely specified.
• Validation sets are deployed when data scientists are tuning the parameters of a classifier. You might start using a validation set to find the total number of hidden units that exist in a predefined neural network.
• Training sets are used exclusively for learning. Many experts will define these as sets that are designed to fit the parameters of the initial classifier.

Segmenting testing, validation and training sets might not seem natural to those who are used to relying on one long inclusive data set in order to ensure that their ML models work in any scenario. Nevertheless, it’s vital to have them separated as much as possible. Test data management should always be part of your QA workflows. On top of this, it’s important to keep an eye on how a model responds as it learns from the data even if it does appear that accuracy increases over time. This is because there are several high-quality insights an operator can derive from the learning process.

Taking a Closer Look at Weights During the Training Process

An ideal model will enjoy lower losses and a higher degree of accuracy over time, which is often more than enough to please the data scientists that develop them. However, you can learn more by taking a close look at what areas are receiving the heaviest weights during training. A buggy piece of code could produce outcomes where different potential choices aren’t given different weights. Alternatively, it could be that they’re not really stacked against one another at all. In these cases, the overall results might end up looking realistic when they’re actually errant. Finding bugs in this way is especially important in a world where ML agents are being used to debug conventional software applications.

Taking a closer look at the weights themselves can help specialists to discover these problems before a model ever makes its way out into the wild. Debugging ML models as though they were application software will never work simply because so many aspects of their neural networks come from exclusively learned behaviors that aren’t possible to decompose into something that could be mapped on a flowchart. However, it should be possible to detect certain types of problems by paying close attention to these weights.

Developing any piece of software takes quite a bit of time, and the fact that ML models have to be trained means that they’ll often take even longer. Give yourself enough lead time and you should find that your testing, validation and training sets split evenly into different neat packages that make the process much simpler.