Ajay Sarangam

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The concept of stochastic is important in machine learning algorithms and is to be understood properly to interpret the behaviour and performance of several machine learning algorithms effectively. Here’s a concise guide to make you understand the concept of stochastic in machine learning and how it is different from non-deterministic.

Before we proceed on to the concept of stochastic in machine learning, let’s first understand the stochastic meaning.

**Stochastic****Stochastic vs. Random, Probabilistic, and Non-deterministic****Stochastic Meaning in Machine Learning**

Stochastic is a term originating from Greek stokhos, “a guess, aim”, is well described by a random probability distribution. Stochastic meaning is being or having a random variable. It refers to a variable process where the outcome has some degree of uncertainty. A stochastic model is any model having some element of randomness. A stochastic model is a tool for estimating probability distributions of potential outcomes by allowing or random variation in one or more inputs over time.

The random variation is usually based on fluctuation observed in historical data for a selected period using standard time- series techniques. Distributions of potential outcomes are derived from a large number of simulations (stochastic projections) which reflect the random variation in the inputs. Although, it is different from “deterministic” but is closely related to “randomness” and “probabilistic.”

Now let’s understand the terms- “random,” “probabilistic,” and “non-deterministic” which are also used as a notion of uncertainty, and how they differ from the term- “stochastic”.

Let’s get a better understanding of stochastic by comparing it with other related terms which are sometimes used as a synonym for stochastic. Stochastic is used as a synonym for random and probabilistic whereas non-deterministic is different from stochastic.

**Stochastic vs. Random**

Generally, stochastic is used as a synonym for random.

Random refer to having unpredictable outcomes and, in the ideal case, all outcomes are equally probable which means there is no dependence on the other observation, for example, tossing a fair coin whereas stochastic is used when we focus on the probabilistic nature of the variable which is randomly determined.

**Stochastic vs. Probabilistic**

Generally, the terms stochastic and probabilistic are used interchangeably. Probabilistic is probably the wider concept. Stochastic is dependent on some past event such as changes in stock price is dependent on the price of the preceding day whereas probabilistic is independent of other observations, for instance, winning lottery numbers are designed to be independent of each other.

**Stochastic vs. Non-deterministic**

Deterministic is the variable or process where the outcome of an event can be determined from the current event. In simple words, we can say that in a deterministic model, nothing is random in it. In contrast, non-deterministic is a variable or process where the same input can give different outcomes.

Generally, stochastic is used as a synonym for non-deterministic because there outcome is uncertain. Stochastic is slightly different from non-deterministic in the way that we can do analysis using the tools of probability, such as expected outcome and variance. So, describing a variable as stochastic is a stronger claim than describing it is non-deterministic.

Many machine learning algorithms and models are described in terms of being stochastic because

it makes use of randomness during optimization and learning.

Let’s understand the nature of stochastic algorithms and the source of uncertainty in machine learning.

**Stochastic Problem Domains**

Uncertainty and stochasticity can arise from many sources. The uncertainty arises from an objective function that is subject to random errors or when the sample fails to represent the whole aspects of the domain.

**Stochastic Optimization Algorithms**

It refers to an optimization algorithm that makes use of randomness to optimize a target function that itself has statistical noise. Simulated Annealing, Genetic Algorithm, and Particle Swarm Optimization are some of the common examples of stochastic optimization algorithms.

**Stochastic Learning Algorithms**

Stochastic Gradient Descent and Stochastic Gradient Boosting are the two common and very popular algorithms used in many machine learning algorithms.

Stochastic Gradient Descent is an optimization algorithm that optimizes the parameters of a model, as an artificial neural network whereas stochastic Gradient Boosting is an ensemble algorithm that ensembles the decision tree algorithm.

**Stochastic Algorithm Behaviour**

The nature of various machine learning algorithms is stochastic as it makes use of randomness or probabilistic. The behaviour of machine learning algorithms is generally seen on non-linear and complicated methods used for classification and regression predictive modelling problems. The stochastic nature of nonlinear machine learning algorithms can be difficult for a newbie who believes that learning algorithms will be deterministic.

In this post, we have covered the concept of stochastic, how it is different from related terms- Random, Probabilistic, and Nondeterministic, and Stochastic meaning in a machine. We hope, this article will serve you as a concise guide and help you understand the foundation concept of stochastic in machine learning.

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