Predictive Modeling Definition

What Is Predictive Modeling?

Predictive modeling uses known results to create, process, and validate a model that can be used to forecast future outcomes. It is a tool used in predictive analytics, a data mining technique that attempts to answer the question, "what might happen in the future?"

Key Takeaways

  • Predictive modeling uses known results to create, process, and validate a model that can be used to make future predictions.
  • Regression and neural networks are two of the most widely used predictive modeling techniques.
  • Companies can use predictive modeling to forecast events, customer behavior, and financial, economic, and market risks.

Understanding Predictive Modeling

By analyzing historical events, companies can use predictive modeling to increase the probability of forecasting events, customer behavior, and financial, economic, and market risks.

Rapid digital product migration has created a sea of readily available data for businesses. Companies utilize big data to improve the dynamics of customer-to-business relationships. This vast amount of real-time data is retrieved from social media, internet browsing history, cell phone data, and cloud computing platforms.

However, data is usually unstructured and too complex for humans to analyze quickly. Due to the sheer volume of data, companies use predictive modeling tools—often via computer software programs. The programs process vast amounts of historical data to assess and identify patterns within. From there, the model can provide a historical record and an assessment of what behaviors or events are likely to occur again or in the future.

Financial analysts can use predictive modeling to estimate investing outcomes based on quantified characteristics surrounding the financial data being modeled.

History of Predictive Modeling

Predictive modeling is likely to have been used as long as people have had information, data, and a method for using it to view possible outcomes. Modern predictive modeling is rumored to have started in the 1940s, with governments using early computers to analyze weather data. As software and hardware capabilities increased over the following decades, large amounts of data became storable and more easily accessed for analysis.

The internet and its connectivity allowed enormous volumes of data to be collected, shared, and analyzed by anyone with access to it. As a result, modeling has evolved to encompass nearly all aspects of business and finance. For instance, companies use predictive modeling when creating marketing campaigns to gauge customer responses, and financial analysts use it to estimate trends and events in the stock market.

Types of Predictive Modeling

Several different types of predictive modeling can be used to analyze most datasets to reveal insights into future events.

Classification Models

Classification models use machine learning to place data into categories or classes based on criteria set by a user. There are several types of classification algorithms, some of which are:

  • Logistic regression: An estimate of an event occurring, usually a binary classification such as a yes or no answer.
  • Decision trees: A series of yes/no, if/else, or other binary results placed into a visualization known as a decision tree.
  • Random forest: An algorithm that combines unrelated decision trees using classification and regression.
  • Neural networks: Machine learning models that review large volumes of data for correlations that emerge only after millions of data points are reviewed.
  • Naïve Bayes: A modeling system based on Bayes' Theorem, which determines conditional probability.

Clustering Models

Clustering is a technique that groups data points. It is assumed by analysts that data in similar groups should have the same characteristics, and data in different groups should have very different properties. Some popular clustering algorithms are:

  • K-Means: K-means is a modeling technique that uses groups to identify central tendencies of different groups of data.
  • Mean-Shift: In mean-shift modeling, the mean of a group is shifted by the algorithm so that "bubbles," or maxima of a density function, are identified. When the points are plotted on a graph, data appear to be grouped around central points called centroids.
  • Density-based Spatial Clustering With Noise (DBSCAN): DBSCAN is an algorithm that groups data points together based on an established distance between them. This model establishes relationships between different groups and identifies outliers.

Outlier Models

A dataset always has outliers (values outside its normal values). For instance, if you had the numbers 21, 32, 46, 28, 37, and 299, you can see the first five numbers are somewhat similar, but 299 is too far from the others. Thus, it is considered an outlier. Some algorithms used to identify outliers are:

  • Isolation Forest: An algorithm that detects few and different data points in a sample.
  • Minimum Covariance Determinant (MCD): Covariance is the relationship of change between two variables. The MCD measures the mean and covariance of a dataset that minimizes the influence outliers have on the data.
  • Local Outlier Factor (LOF): An algorithm that identifies nearest neighboring data points and assigns scores, allowing those furthest away to be identified as outliers.

Time Series Models

Commonly used before other types of modeling, time series modeling uses historical data to forecast events. A few of the common time series models are:

  • ARIMA: The autoregressive integrated moving average model uses autoregression, integration (differences between observations), and moving averages to forecast trends or results.
  • Moving Average: The moving average uses the average of a specified period, such as 50 or 200 days, which smooths out fluctuations.

Applications of Predictive Modeling

Predictive analytics uses predictors or known features to create models to obtain an output. There are hundreds, if not thousands, of ways predictive modeling can be used. For example, investors use it to identify trends in the stock market or individual stocks that might indicate investment opportunities or decision points.

One of the most common models investors use is an investment's moving average, which smooths price fluctuations to help them identify trends over a specific period. In addition, autoregression is used to correlate an investment or index's past values with its future values.

Predictive modeling also helps investors manage risk by helping them identify the possible outcomes of different scenarios. For example, data can be manipulated to forecast what might happen if a fundamental circumstance changes. Investors can create strategies to deal with changing markets by identifying possible outcomes.

Predictive Modeling Tools

Predictive models are also used in neural networks such as machine learning and deep learning, which are fields in artificial intelligence (AI). The neural networks are inspired by the human brain and created with a web of interconnected nodes in hierarchical levels, representing the foundation for AI. The power of neural networks lies in their ability to handle non-linear data relationships. They are able to create relationships and patterns between variables that would prove impossible or too time-consuming for human analysts.

Other predictive modeling techniques used by financial companies include decision trees, time series data mining, and Bayesian analysis. Companies that take advantage of big data through predictive modeling measures can better understand how their customers engage with their products and can identify potential risks and opportunities for the company.

Advantages and Disadvantages of Predictive Modeling

Predictive Modeling Pros and Cons

  • Easy to generate actionable insights

  • Can test different scenarios

  • Increases decision-making speed

  • Computations can be inexplainable

  • Bias due to human input

  • High learning curve

Advantages Explained

  • Easy to generate actionable insights: Predictive modeling allows you to view information about your data that you might not see otherwise, enabling you to make more informed decisions.
  • Can test different scenarios: Data can be manipulated or changed to test various scenarios to assess the influence changes might have on your data and models.
  • Increases decision-making speed: Decisions can be reached much faster because millions of data points can be analyzed much quicker, and future trends or circumstances can be theorized within minutes or hours.

Disadvantages Explained

  • Computations can be inexplainable: You may not be able to interpret the results once you create a predictive model.
  • Bias due to human input: Bias is introduced into modeling because humans are involved in setting parameters and criteria.
  • High learning curve: Learning to create predictive models and/or interpret the results can be a lengthy process because you have to understand statistics, learn the jargon, and possibly even learn to code in Python or R.

What Are Predictive Modeling Algorithms?

An algorithm is a set of instructions for manipulating data or performing calculations. Predictive modeling algorithms are sets of instructions that perform predictive modeling tasks.

What Is the Biggest Assumption in Predictive Modeling?

The most significant assumption in predictive modeling is that future data and trends will follow past occurrences.

What Is an Example of Predictive Modeling in Healthcare?

Predictive modeling can be used for many purposes, especially in health insurance. For example, it can help insurance companies calculate the costs for specific customers based on their health, lifestyle, age, and other circumstances.

The Bottom Line

Predictive modeling is a statistical analysis of data done by computers and software with input from operators. It is used to generate possible future scenarios for entities the data used is collected from.

It can be used in any industry, enterprise, or endeavor in which data is collected. It's important to understand that predictive modeling is an estimate based on historical data. This means it is not foolproof or a guarantee of a given outcome—it is best used to weigh options and make decisions.

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