The use of traditional or deterministic machine learning models has the ability to come up with predictions only for a predefined or specified event. That is where probabilistic machine learning plays a vital role by not only predicting outcomes for certain events rather coming up with predictions for uncertainties considering various parameters for prediction. This article briefs on the different ways a machine learning model learns from the data with uncertainty involving the concept of probability theory.  We will also focus on why the probabilistic machine learning model outperforms the traditional methods.

Table of contents

1. What is probabilistic machine learning?
2. Different ways a machine learning model learns?
3. Pros/cons of the traditional learning method
4. The necessity for probabilistic machine learning
5. Why is probabilistic learning the best?
6. Summary

Let’s start the discussion with what actually is a probabilistic learning approach. 

What is probabilistic machine learning?

Probabilistic machine learning is one of the techniques a machine learning model learns from the fitted data and yields predictions not only for particular classes or instances but also ensures to address the issues with uncertainties in general and helps us in yielding predictions with respect to all the classes considered.

The linear growth of data may be one of the reasons why probabilistic machine learning models yield the right outcomes considering the possible outcomes of each of the features and the different classes in the instance of the data.

Are you looking for a complete repository of Python libraries used in data science, check out here.

Different ways a machine learning model learns

There are various ways a machine learning model learns its data. Some of them include Deterministic machine learning, Stochastic machine learning, and Probabilistic machine learning.

Deterministic Machine learning

As the name suggests deterministic machine learning is one of the ways where a machine learning model learns its data in the form of possibilities of natural outcomes possible. This way of learning may help in yielding reliable results for the various input parameters of the data utilized, and the learning happens in an iterative process where more the learning from the data acquired by the machine learning model developed better are the outcomes.

Pros and cons 

One major advantage of deterministic learning is the learning process may converge quicker yielding a simpler model but the model when considered for uncertain data or changing events may be responsible for wrong outcomes as in this process learning process happens without considering the randomness of features.

Stochastic Machine Learning

As in the name “Stochastic”, this is a way where the machine learning model learns by considering the possibilities of randomness and possible future uncertainties. So as the model learns about randomness and possible uncertainties, the model will be responsible for yielding predictions considering all the likeliness factors of the input data used.

But even if the model learns with respect to randomness and uncertainties there are some drawbacks to using this type of learning. The pros and cons of this type of learning are mentioned below.

Pros and cons

As mentioned the stochastic learning process happens by considering all the uncertainties in the data. But the fact considering the possible uncertainties, the consideration happens with respect to certain bias factors, and only the most extreme uncertainties are considered leaving behind factors with considerable uncertainty, and this would be a concern because data the model learns may possibly change over time and the let of uncertainties by stochastic machine learning may stand off to be the most extreme uncertain information or data.

The necessity for probabilistic machine learning

So as we have an overview of the individual learning factors of both the deterministic and stochastic learning models and their respective concerns of pros and cons, probabilistic machine learning is one such effective learning technique for the machine learning model developed as it learns all the uncertainties of the data without any bias and considers the effect of possible randomness in future and help in yielding the right outcomes in the testing phase of the machine learning model developed.

One of the most commonly used probabilistic classifier models is the Naive Bayes Classifier model which facilitates addressing the possible uncertainties through required conditions of randomness as it basically obeys the property of Conditional Probability where for any certain independent assumptions made for uncertainties the probability distribution considers the possible likelihood of all the parameters to be estimated.

Estimating certain parameters and possible outcomes becomes easier by using the Naive Bayes classifier algorithm but as in the name “Naive”, it has certain limitations with respect to the assumptions of the target variable. But during the learning process, specific conditions for possible uncertainties can be mentioned to evacuate the possible outcomes of issues associated with a bias for most uncertain events.

Due to the various advantages of probabilistic machine learning, there are various frameworks supporting the same. Some of them include:

• STAN – A Bayesian statistical framework
• Tensorflow probability – A compact framework of Tensorflow
• Pyro – A universal probabilistic learning framework
• PyMC3 – Open Source framework for probabilistic learning

Why is probabilistic machine learning the best?

To answer this question let’s keep in mind the common issues a typical machine learning model in production would face. Say for supposing data provided is insufficient. This is where probabilistic learning addresses the issue of data scarcity by addressing the randomness and the possible uncertainties of the data employed and probabilistic learning 

So once required data is available the next possible concern would be a linear expansion of the model. So as probabilistic learning facilitates linear expansion, possible concerns with model scalability are also addressed as the model has already learned for possible uncertainties. The issues of bias uncertainty are addressed and the machine learning model exhibits a high degree of representation of convergence for uncertainties for random events.

The probabilistic model easily converges with small changes in data as it is pretrained for possible uncertainties.

Attention to the most relevant information is ensured in probabilistic learning as it learns through various randomness and uncertainty by iterating through various parameters.

Transparency and reliability of the model in production are ensured as the model has learned to yield the right predictions for any possible uncertainties.

So these are some points which make probabilistic learning one of the effective learning techniques a machine learning model learns and help us yield reliable models for production.s

Summary

So among the various methods of making a machine learning model learn the data, currently probabilistic machine learning appears to be the most appealing technique as it adheres to learning for all possible uncertainties without any bias factors for all the possible uncertainties. Making a machine learning model learn the probabilistic way may help in yielding reliable models for better predictions for business-driven solutions and evacuate the concerns associated with serious consequences of faulty predictions.