Regression in machine learning applies mathematical methods to help data scientists predict a continuous value. That means numbers. Price, Revenue, Age, or Quantity, for example.
So, regression models have a wide range of applications, from the home's value to a vehicle's gas consumption, predicting the profitability of products to how many eggs your chicken will lay over a certain period.
Many methods and models are used in regression analysis, each with specific merits. These include:
But in its purest sense, regression in machine learning relies heavily on data from independent variables (different attributes and columns in our dataset) to predict its relationship with the dependent variable (what we want to predict).
Despite how complicated it sounds, regression in machine learning is one of the essential tools used in machine learning predictions.
Regression yields two purposes—interpolation and extrapolation.
Interpolation helps predict missing data. For example, you've tracked your monthly sales for the last year. If you want to know exactly how much you earned in the third week of the first month, interpolation will help you estimate the missing data given the information you've collected.
On the other hand, extrapolation helps predict future data. Extrapolation is used to help you see beyond your existing data. Using the same example, extrapolation can help estimate how much you'll earn the following year.
Overall, regression helps predict specific outcomes for numeric variables, whether outside your existing data range or filling out the missing information.
Machine Learning Regression Applications
Regression models are used in machine learning to determine a wide range of data points.
This way, you can make business decisions such as how much inventory you need to order or how profitable a prospective product is.
That said, machine learning regression can also be applied to other areas of your business. Let's take a look at its most common uses below.
Machine learning regression can be used to predict certain outcomes. It answers a specific question using a historical data set that is often derived from forecasts. In business, predictions can help determine the likelihood of customer retention and whether or not a particular niche is profitable.
Basically, it helps you make guesses with great accuracy. For example, if you predict that a customer segment will likely make a high-value purchase, you can target them with specific campaigns.
A sub-discipline of predictions, forecasts use time-series data (temporal dimensions) to make accurate predictions. However, it mainly focuses on predicting actual values based on historical trends instead of future behavior.
Forecasting is typically used to determine company turnover rates or product demand. You can also use it to help analyze budget allocations to minimize losses in your business.
Time Series Modeling
Time series analysis uses a model to predict future numbers based on previous results. It's a common business forecasting technique often used to foresee sales, economic changes, inventory studies, or similar.
Many business owners use it to make predictions using existing data. For example, you can use it to predict how much a specific product will earn in sales over a few months or years.
Determining Causal-Effect Relationship Of Variables
Establishing the causal relationship between independent and dependent variables is the primary goal of most research projects. Causation is used to see how independent variables affect your dependent variables. Consider it a way to prove that a relationship exists.
For instance, suppose you want to determine whether a new product will do well in a segment, then before committing to a full-scale launch, the intelligent action would be to test the campaign. To do so, you would target a minor area or segment, conduct a survey, and measure sales volume, and increase in leads or queries. These responses would then help you determine the relationship between the product and customer interest.
Often referred to as the "missing variable" in a problem, causal relationships account for events you can foresee.
Determining the causal relationship between variables can also help you navigate making complex business decisions. For example, if you were prompted to choose between two products, an overview of causal relationships can help you determine which one would perform better over time.
You'll hear many new terms and words when it comes to regression in machine learning. And if you have no idea what you're looking at, you may feel lost and confused. We've compiled some of the most important terms below to familiarize yourself with what they mean.
This is the column that you want to predict in your analysis. It's dependent on existing data and other factors that you'll incorporate into the analysis. Data Scientists commonly denote them using a Y.
Example: "sales amount."
Also called the predictor, an independent variable is taken from existing data. Changes in the dependent variable will vary according to the value of your independent variable. Data Scientists commonly denote them using an X.
Example: "country", "time", "product type".
These are observations with either exaggerated or downplayed values compared to other results. These are generally avoided since they may hinder an accurate result.
Why Is Regression in Machine Learning Important?
Since all decisions you make today will have long-term effects on your business, it is imperative to look at data that shows how each decision affects your company's future. Regression analysis can help you with this, ensuring that you make smarter decisions.
Regression in machine learning has many applications, from helping you answer business-related questions to figuring out how well a product performs. Regression analytics can walk you through making some of the most life-changing decisions for your business, from customer retention rates to the estimated trends in your niche months from now.
These types of analytics are complicated to understand for someone who doesn't have experience with analytics.
That's why looking into trusted platforms and software that help you leverage regression analysis without much knowledge is essential.
This blog post provides insights based on the current research and understanding of AI, machine learning and predictive analytics applications for companies. Businesses should use this information as a guide and seek professional advice when developing and implementing new strategies.
At Graphite Note, we are committed to providing our readers with accurate and up-to-date information. Our content is regularly reviewed and updated to reflect the latest advancements in the field of predictive analytics and AI.
Hrvoje Smolic, born in 1976 in Zagreb, Croatia, is the accomplished Founder and CEO of Graphite Note. He holds a Master's degree in Physics from the University of Zagreb. In 2010 Hrvoje founded Qualia, a company that created BusinessQ, an innovative SaaS data visualization software utilized by over 15,000 companies worldwide. Continuing his entrepreneurial journey, Hrvoje founded Graphite Note in 2020, a visionary company that seeks to redefine the business intelligence landscape by seamlessly integrating data analytics, predictive analytics algorithms, and effective human communication.
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