Machine Learning is all about conducting controlled experiments to detect algorithm configurations that can resolve a predictive modeling problem.
No wonder then that machine learning enthusiasts and experts often ask themselves the question, ‘what is a confounding variable’ before implementing auto ML. Even in a learning algorithm, randomness in the form of random initialization or random choices must be deployed in the learning stage.
Areas of Application
Machine learning can be applied in many fields, including data analytics, predictive analysis, language processing, computer vision, speech recognition, and neuroscience. Many such predictive models are now in use in research being conducted in clinical neuro-imaging to help diagnose diseases and predict prognosis. Machine learning is also used in non-clinical domains to explore relationships between biology and cognitive potential.
The Major Challenge
The major challenge is the several ingredients of the problem; confounding variables cannot be controlled or held constant. Using random initialization or random choices in an algorithm is an excellent example of a confounding variable.
One probable solution is using randomness, which is already a standard as applied machine learning is concerned. For example, once we understand why randomness is used for managing confounding variables in medicine using random clinical trials, we’ll understand the rationale behind using randomness in controlled experiments. The National Center for Biotechnology Information (NCBI), a division of the National Library of Medicine (NLM), states that confounding is often referred to as “a mixing of effects.”
Confounding Variables Influence Outcomes
Confounding variables can impact the result of an experiment by producing:
- Unfounded correlations
- Escalating discrepancy
- Bringing in a prejudice
To confound matters further, a confounding variable can be known or unknown.
They are probably known as confounding variables because they are known to correlate with dependent and independent variables. One other highlight of the confounding variable is that it affects observations differently and can correlate positively or negatively with the dependent or independent variable.
Machine Learning and Confounding
In applied statistics, the concern is with the consequence of independent and dependent variables in data. While statistical methods are deployed to identify and outline these relationships, confounding variables will virtually invalidate such identifications.
That’s probably why practitioners who swear by machine learning don’t give much importance to the statistical precision of a predictive model; they are more interested in such a model’s skill. Hence, confounding variables play a vital role in data selection and preparation but don’t consider descriptive statistical models to hold much significance. However, the fact of the matter is that confounding variables are essential in applied machine learning.
Assessment and Analysis
The assessment of a machine learning model is more an analysis of independent and dependent variables, and that’s why it is dependent on confounding variables. Hence, to comprehend the choice and interpretation of machine learning model evaluation, it is vital to be keenly aware of what is a confounding variable and how it applies to machine learning experiments.
Here are some examples of what impacts the assessment of a machine learning model:
- The selection of data preparation schemes
- The preferred samples in the training dataset
- The preferred samples in the test dataset
- The preferred learning algorithm
- The choice of initialization and configuration of the learning algorithm
All of the above choices will influence the dependent variable in such a machine learning experiment, which is the desired metric to appraise the model’s skill while making predictions.
Randomization in machine learning algorithms is a feature that enhances the performance of the model over other methods but doesn’t mar or blemish it. Also, randomness manifests in machine learning at different levels, always endeavoring to enhance the performance of classical methods.
Summing it Up
Confounding variables can be addressed using a randomization tool. Applied machine learning deals with controlled experiments that are affected by confounding variables. Controlling for confounding variables in ML experiments depends on the randomization of experiments.