Whenever the accuracy of predictions generated from fresh input values “drifts” from the efficiency during the training phase, this is known as model drift. Model drift may be divided into two categories:
When the statistical features of the goal (dependent) variable change, this is referred to as concept drift.
When the statistical features of the independent variables change, data drift occurs.
Both forms of drift, as one might imagine, cause model performance to deteriorate.
Machine learning algorithms can provide prediction outcomes that differ from the initial parameters established during the training period, or “drift.” The corrective steps necessary to bring forecasting accuracy closer to an appropriate standard will be determined by identifying the type of drift.
Importance of Model drift
To maintain accurate forecasts and so inspire continuing use of AI across a company, it is vital to evaluate performance for model drift. Businesses can use a variety of methods to keep track of model performance, including:
- Creating performance dashboards based on model KPIs
- Detecting data drift in comparison to a reference set
- Using both champion and challenger models to allow for “hot replacement” with one version with another when conditions change.
Detection of Model Drift
Matching the expected values from an ML model to the real amounts is the most accurate approach to discover model drift. As the projected values wander farther and further from the actual values, the model’s accuracy deteriorates.
The F1 score is a typical statistic used by data scientists to assess a model’s accuracy since it considers both recall and precision. However, depending on the scenario, some measures are more important than others. As a result, you’ll know your system is drifting when a specific measure goes below a certain threshold!
It’s not always practical to keep track of a model’s correctness. Obtaining the projected and actual matched data gets substantially more difficult in some cases. You can also rely on the following alternatives:
Test of Kolmogorov-Smirnov: The K-S experiment investigated the accumulated densities of two data sets, in this instance the data for training and the post-training data, and is a nonparametric test. The hypothesis for this study is that the datasets’ distributions are identical. You can assume that your system has drifted if the null hypothesis is rejected.
The Population Stability Index is a statistic that measures how the distribution of a variable has changed over time. It’s a common statistic for tracking changes in a population’s features and, as a result, detecting model deterioration.
Finally, the z-score may be used to evaluate the attribute distribution between training and real data. If a lot of live data points of a particular variable have such a z-score of +/- 3, for example, the variable’s distribution may have moved.
Identifying model drift is merely the first step; managing model drift is the next. There are two primary approaches to this.
The first option is to retrain your model regularly. When you realize that a model declines every three months, you can opt to retrain it every three months so that it never drops below a specific level of accuracy.
Online learning is another technique to combat model drift. Making an ML model adapt in real-time is known as online learning. Instead of model training with batch data, it takes in input as soon as it is available in sequential order.