Author:
CEO & Co-Founder
Reading time:
Predictive analytics is one of the most fascinating aspects of our work and the whole machine learning discipline. These tools help us predict various situations in your company or assess the probability of a given scenario or course of events. However, there is something we all have to remember about–performance evaluation of predictive models. In fact, this is what makes them so useful. What do we mean by performance evaluation? In this article, we are going to take a closer look at this subject.
In general, predictive models are based on so-called supervised machine learning techniques. These techniques are primarily:
First off, let’s take a look at these techniques. A short reminder will help us understand why performance evaluation is critical and how to do it.
As an introduction to this blog post, let’s remind ourselves that supervised machine learning methods are used primarily when you need to predict or explain data you possess. In fact, the supervised machine learning techniques group and interpret data based exclusively on input data. A supervised algorithm doesn’t matter which one, takes a known set of input data and known responses to the data (output) and trains a model to produce predictions.
The most extensively used ML supervised techniques are classification and regression. For instance, the supervised ML techniques can be used to predict/assess whether the company will win the specific contract or how many users will sign up for the newsletter over the next year. There’s also the ensemble method that combines various supervised techniques–it can incorporate classification and regression into one model or just take several regression/classification techniques to improve their accuracy.
In our past blog posts, we sometimes mentioned regression. This technique is used primarily to predict/explain a specific value based on prior data. Generally speaking, there are five major types of regression:
These techniques serve different purposes. For instance, the technique called the decision tree helps in making decisions and is commonly used in operations research, business intelligence, and strategic planning. With a decision tree, you can assess the probability of a specific event/scenario and devise a strategy to deal with it or to prevent it from happening.
Moreover, you can use regression techniques to predict salary levels, disease spread, property values, and many other different things. The key is always the same–you have to have a set of prior data that’s a basis for the predictive models.
In short, classification helps predict or explain a class value. The classification techniques help companies in estimating the probability of an occurrence of a specific event based on one or more inputs. For instance, classification enables companies to predict whether a given customer will buy a product.
Such a prediction is typically based on their behavior on the website and historical data regarding their behavior and/or past purchases. And let’s take another example. Classification helps companies assess whether the company will win the contract. In such a situation, the output is a number between 0 and 1, where 0 means “no”, and 1 means “yes”. However, everything above 0.5 brings you closer to the answer “yes”.
What’s characteristic regarding the classification models? The output can be ascribed to two (yes, no) or, in some situations, three classes. And while classification predicts a discrete class label, the aforementioned regression predicts a quantity.
And then, we have the ensemble methods, which combine regression and classification.
The purpose of ensemble methods is to improve the accuracy of the previously analyzed techniques and obtain more high-quality results. The main idea behind ensemble methods is to reduce the variance and bias that’s typical of every single machine learning technique.
You see, every single ML model can turn out to be accurate under certain circumstances but inaccurate under others. Now, because ensemble methods take at least two different predictive models into consideration, the bias decreases. How does it look like in real life? Take the example of random forest, which is a textbook ensemble method. Random forests combine many decision trees (regression models). As a result, the random forest technique is more accurate than just one decision tree.
In today’s business environment, predictive models play a crucial role. They help companies make more informed decisions and analyze various scenarios. We have made a list of industries and sectors that extensively use predictive models:
Of course, the list of industries and sectors that commonly use predictive models is much longer. Similar solutions are used in stock markets, real estate, marketing, software development, production, and many other branches of business.
For obvious reasons, predictive models are useful only when they produce accurate, reliable outcomes. And this is what, in short, performance evaluation is all about. There are various evaluation metrics that are strictly correlated with machine learning techniques.
They come in handy, especially when you are working with supervised ML techniques because all the data you need is readily available. These values help you in the performance evaluation of your predictive models.
What you need to know is that there is a fundamental difference between predictive model performance evaluation in regression and in classification. In a few moments, we will show you the most popular evaluating methods for both these ML techniques.
Model evaluation is an important step in the creation of a predictive model. It aids in the discovery of the best model that fits the data you have. It also considers how well the selected model will perform in the future. In general, there are two major methods of evaluating predictive models:
Now, we are going to analyze both these models.
With the hold-out predictive method, you have three subsets of data:
Here, the main idea is to split up your dataset into a training and testing set. The test set allows you to see how well your predictive model performs on unseen data. Typically, you use 80% of your data for training and the remaining 20% of the data for testing[1].
The cross-validation technique comes in handy when only a limited amount of data is available. Here, you divide data into k groups. Again, one of the k groups is used as the test set, and the rest are used as the training set. In short, the predictive model is trained on the training set and then scored on the test set.
We could say that cross-validation is frequently the preferred performance evaluation method. That’s because it offers the possibility to train your models on multiple splits, which gives a more thorough insight into how your predictive models will perform in the future (on unseen data).
When it comes to regression model evaluation, it’s all about predicting a quantity. Therefore, you can use several metrics to measure your model’s performance:
Concerning classification, we try to predict or explain a class value. Therefore, we can use several evaluation techniques:
As you can see, predictive modeling is quite an extensive field that’s used to support a wide range of companies and organizations. And thanks to analyzed predictive statistical models, companies using predictive models can improve their results and make more informed decisions. If you’d like to find out how predictive analytics can help you with everyday work and development–feel free to contact us.
We are an AI consulting company. We deal with predictive models every day and know how to use them for your company’s good and growth. The Addepto team is at your service!
[1] Eijaz Allibhai. Hold-out vs. Cross-validation in Machine Learning. Oct 3, 2018. URL: https://medium.com/@eijaz/holdout-vs-cross-validation-in-machine-learning-7637112d3f8f. Accessed Mar 25, 2021.
[2] Divya Singh. What is Predictive Model Performance Evaluation. Mar 19, 2019. URL: https://medium.com/@divyacyclitics15/what-is-predictive-model-performance-evaluation-8ef117ae0e40. Accessed Mar 25, 2021.
[3] L.V. 11 Important Model Evaluation Techniques Everyone Should Know. February 20, 2016. URL: https://www.datasciencecentral.com/profiles/blogs/7-important-model-evaluation-error-metrics-everyone-should-know. Accessed Mar 25, 2021.
Category: