How to learn from uncertainty using probabilistic machine learning?

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


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

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

What is probabilistic machine learning?

Probabilistic machine learning is one of the techniques that a machine learning model learns from the fitted data and produces predictions not only for particular classes or instances but also guarantees to solve the problems with uncertainties in general and helps us produce predictions with respect to all classes. considered.

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

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Different ways a machine learning model learns

There are different ways for a machine learning model to learn 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 in which a machine learning model learns its data in the form of possibilities of possible natural outcomes. This learning method can help achieve reliable results for the different data input parameters used, and the learning occurs in an iterative process where more learning from the data acquired by the machine learning model developed is better.

Advantages and disadvantages

One of the main advantages of deterministic learning is that the learning process can converge faster, resulting in a simpler model, but the model, when considered for uncertain data or changing events , may be responsible for erroneous results, because in this process the learning process occurs. regardless of the randomness of features.

Stochastic machine learning

As in the name “stochastic”, it is a way in which the machine learning model learns by taking into account the possibilities of randomness and possible future uncertainties. Thus, as the model learns about randomness and possible uncertainties, the model will be responsible for producing predictions taking into account all the probability factors of the input data used.

But even though the model learns against chance and uncertainty, there are some drawbacks to using this type of learning. The advantages and disadvantages of this type of learning are mentioned below.

Advantages and disadvantages

As mentioned, the stochastic learning process occurs by taking into account all the uncertainties in the data. But the fact given the possible uncertainties, the consideration is against certain bias factors, and only the most extreme uncertainties are considered to leave behind factors with considerable uncertainty, and that would be of concern because the data learned by the model can eventually change over time and stochastic machine learning accounting for uncertainties can turn out to be the most extreme uncertain information or data.

The need for probabilistic machine learning

Thus, as we have an insight into the individual learning factors of deterministic and stochastic learning models and their respective concerns regarding pros and cons, probabilistic machine learning is one of the effective learning techniques for the machine learning model developed as it learns all the uncertainties of the data without any bias and considers the effect of possible coincidence in the future and help to get the right results in the testing phase of the developed machine learning model.

One of the most commonly used probabilistic classifier models is the Naive Bayes classifier model which makes it easier to deal with possible uncertainties through randomness requirements because it essentially obeys the property of Conditional probability where for some independent assumptions made for the uncertainties, the probability distribution considers the possible probability of all the parameters to be estimated.

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

Due to the various advantages of probabilistic machine learning, there are different frameworks that support it. Some of them include:

  • STAN – A Bayesian Statistical Framework
  • Tensorflow Probability – A Compact Tensorflow Framework
  • 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 common issues that a typical machine learning model in production would face. Suppose that the data provided is insufficient. This is where probabilistic learning addresses the issue of data scarcity by addressing the randomness and possible uncertainties of the data used and probabilistic learning

Thus, once the required data is available, the next possible concern would be a linear expansion of the model. Thus, since probabilistic learning facilitates linear expansion, possible problems with model scalability are also addressed because the model has already learned about any uncertainties. The challenges of uncertainty bias are addressed and the machine learning model has a high degree of representation of the convergence of uncertainties for random events.

The probabilistic model easily converges with small changes in data because it is pre-trained for possible uncertainties.

Pay attention to the most relevant information is assured in probabilistic learning because it learns through various hazards and uncertainties by iterating through various parameters.

Transparency and reliability of the model in production are assured because the model has learned to produce the correct predictions for all possible uncertainties.

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


So, among the different methods for a machine learning model to learn the data, probabilistic machine learning seems to be the most attractive technique because it adheres to learning for all possible uncertainties without any bias factor for all possible uncertainties. Getting a machine learning model to learn probabilistically can help produce reliable models for better predictions for business-focused solutions and remove concerns associated with serious consequences of incorrect predictions.

Sherry J. Basler