Machine Learning is this ever-growing domain in the world of technology and innovations. Machine learning is fundamentally the procedure that enables computers to learn from data so they can recognize patterns and similarities and make predictions on their own without human intervention. The two major approaches of machine learning modeling are – Deterministic and Stochastic.
In this article, let us try to understand what these approaches are, and what are the key differences between them. By weighing the advantages and disadvantages of both, we’ll also try to determine which circumstances call for which strategy.
Also read: Basics of Machine Learning
What is the Deterministic model?
A system is said to be deterministic if there is no role for randomness in how its future states will emerge. No matter how many times the model is updated, deterministic modeling consistently returns the same results for a given set of inputs, because in this method we create a set of rules and use the same again and again.
- Simple to implement, and maintain
- It is easy to resolve any errors occurring while implementing this type of model
- It can sustain one set of rules for the long term and hence produces satisfactory to average results.
- When working with vast data sets, deterministic techniques tend to be preferred as they can be quicker and more effective.
- A deterministic algorithm might not be successful in delivering accurate predictions if the data fails to comply with a clear set of rules.
- Deterministic models may struggle with complicated, nonlinear relationships in the data since the rules are unbending making it difficult for them to modify effectively to changing data.
What is the Stochastic model?
A system involving probability and randomness to produce predictions is a stochastic machine learning system. The model produces numerous amounts of predictions, and results. The exact process is subsequently carried out multiple times in different circumstances.
- The wide variety of potential outcomes and the corresponding probability of each are provided by stochastic models.
- Data with complicated, nonlinear relationships can be handled via stochastic machine learning.
- Stochastic models work very well with changing/dynamic circumstances since they are readily adaptable to changing data.
- Stochastic methods may be slower and more challenging to understand.
- Computational expenses associated with stochastic models tend to be greater than for deterministic methods.
Comparison between Deterministic vs Stochastic models
After thoroughly understanding what deterministic and stochastic models are, we are able to arrive at a few inferences about how to differentiate between the two types of models and which situations call for either approach.
A deterministic approach has a straightforward and simple structure and may only be employed when the link between variables is known in contrast to a stochastic approach, which deals with the likelihood of probabilities and has a complex and difficult to comprehend structure.
A deterministic model is preferred when factors like speed and efficiency are more concerning whereas a stochastic model is preferred where accuracy is more important than speed.
A deterministic model provides the same output, hence used in models like turning machines, linear regression models with no random errors, Principal Component Analysis (PCA), etc; while a stochastic model provides multiple outputs and likelihood of the result as well hence, this approach is used in more real-life applications like stock price estimation, Monte Carlo Stimulation, etc.
We have discussed both types of modeling – deterministic and stochastic. In conclusion, we can say that both of them have their own specialties and weakness. By looking at the pros and cons list one can decide which method is beneficial for her/him. Also, the type of input data, the certainty of input, and the desired form of result play a crucial role in selecting one of either method.
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Deterministic System – Wikipedia
Stochastic Process – Wikipedia