Introduction
There is no giving second thoughts to the statement – ‘Machine Learning has revolutionized our perception of data’. Understanding probability theory, which is one of the drivers behind machine learning, is crucial given the Machine Learning field’s recent breakthroughs. Taking this into consideration, we will now take a look at the main techniques and applications of probability in machine learning.
Probability Theory
The statistical rule guiding the occurrence of random events in the natural world is found using the mathematical method of probability statistics. As data science and machine learning advance, probability statistical knowledge has grown in prominence and is now utilized extensively across a range of sectors. The idea of random variables and probability distribution is used by probability theory to determine any situation’s outcome. The probabilities and statistics of an event occurring are the main focus of probability theory, a sophisticated area of mathematics.
In simple words, probability is the likelihood of an event taking place. For instance, a person is asked to pick a ribbon from an opaque box containing 20 ribbons of red, blue, yellow and green color, 4 of each without looking inside the box. What are the chances for the person picking up a red ribbon? To answer such questions, we make use of probability theory. Machine Learning models make predictions based on this theory of probability.
Probabilistic Modeling – Role of Probability Theory in Machine Learning
Probability theory finds its application in machine learning to represent complicated and unpredictable systems. To describe the inherent uncertainty in data and to generate predictions based on that data, we make use of probabilistic models. Probabilistic models assign probabilities to different possibilities based on the information available. Think about a scenario in which we are attempting to estimate a stock’s price based on its historical data. We may build a probabilistic model using the provided data that assigns probabilities to several realistic pricing ranges. The future worth of the stock might then be predicted using these probabilities.
Probabilistic modeling takes into account novel circumstances and a broad range of uncertainty without underestimating risks. The effective use of input data for these distribution functions, adequate probability distributions, and proper accounting for the connections and interactions between variables are the three main pillars of probabilistic modeling. The drawback of the probabilistic modeling approach is that it requires thorough development, a procedure that relies on a lot of input data and various assumptions.
Key Techniques of Probabilistic Modeling
Probability-based machine learning models employ a number of essential techniques. The list of such key techniques goes as follows:
1. Bayes’ Theorem
The Bayes theorem, a cornerstone of probability theory, offers a mechanism to modify our opinions about an event in light of fresh information. In machine learning, it is heavily utilized for tasks like classification and prediction.
In the Bayes theorem, we begin with a prior probability, which reflects our pre-evidence opinion of an event. The posterior probability, which reflects our opinion on the event after taking new evidence into account, is then obtained by updating this prior probability in light of new information.
2. Maximum Likelihood Estimation
A probabilistic model’s parameters can be estimated using the maximum likelihood estimation technique using observed data. Finding the values of the model parameters that maximize the likelihood of the observed data is the aim of maximum likelihood estimation.
When performing maximum likelihood estimation, we begin with a preliminary estimate of the model parameters and repeatedly improve these estimates until we identify the values that maximize the likelihood of the observed data.
3. Markov Chain Monte Carlo Methods (MCMC)
A series of algorithms known as Markov chain Monte Carlo (MCMC) approaches is used to sample from complicated probability distributions. In machine learning, MCMC techniques are frequently employed for tasks like Bayesian inference and model fitting.
In MCMC approaches, new samples are iteratively generated using a transition function that depends on the current sample, starting with an initial sample taken from the relevant probability distribution. The resulting sequence of samples is a Markov chain, and by running the chain for a sufficiently long time, we can obtain a sample from the desired probability distribution.
4. Gaussian processes
A potent method for modeling complicated, non-linear systems is the use of Gaussian processes. A Gaussian process provides a probability distribution over functions and it can be used to describe a variety of events, from physical processes to financial data. Gaussian processes can offer a flexible and non-parametric technique to model complicated relationships in data for machine learning applications like regression and classification.
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Applications of Probability-based ML Models
There are numerous uses for probability-based machine learning models in a variety of sectors, including banking, healthcare, and transportation. Some of the major applications of these models are as follows:
1. Detection of Fraud
Machine learning models based on probability can be used to spot fraud in financial transactions. These models can pinpoint transactions that are probably fraudulent and notify financial institutions to further investigate by modeling the probability of fraud based on past data.
2. Diagnosis of a Disease
Based on patient symptoms and medical history, probability-based machine learning models can be utilized in the healthcare industry to diagnose diseases. These models can assist clinicians in making more accurate diagnosis and selecting the most appropriate course of therapy by modeling the probability of various diseases depending on the available data.
3. Maintenance Prediction
Machine learning models based on probability can be used in manufacturing to forecast when machinery is most likely to malfunction. These models can assist businesses in planning maintenance before a failure occurs, minimizing downtime and maintenance costs. The models use sensor data from the equipment to model the chance of failure.
4. Autonomous Driver Assistance Systems (ADAS)
Probabilistic decisions are made by autonomous systems, like self-driving cars by predicting the behavior of other cars and pedestrians. These autonomous vehicles use data gathering techniques and logical reasoning algorithms to determine the likelihood of certain scenarios. By analyzing data obtained from the car sensors, these predictive models simulate the likelihood of different actions and help the autonomous driving system make safer and more accurate decisions.
Advantages and Disadvantages of Probabilistic Modeling
Some of the notable advantages of Probabilistic Modeling are as follows:
- Increased accuracy in predictions: Probabilistic models can provide predictions that are more accurate because they account for data variability and uncertainty. These models can produce predictions that are more solid and trustworthy since they model the probability distribution of the data.
- Better management of missing data: The ability of probabilistic modeling to handle missing data is one of its main benefits. These models may estimate missing variables and still produce precise predictions based on the existing data by employing probabilistic inference.
- Better interpretability: Probabilistic models can give users a more logical grasp of how different variables relate to one another in a dataset. These models are able to find significant features and linkages that might be concealed in other models by modeling the probability distribution of the data.
Despite these advantages of Predictive Modeling, there are certain disadvantages that must be considered.
- Costly to compute: When working with huge datasets, probabilistic models can be very costly to compute. A probabilistic model’s parameter estimation procedure may involve numerous iterations. Besides, the models themselves may be complex and challenging to optimize.
- Assumptions for data distribution: Probabilistic models require assumptions regarding the data distribution, and if these assumptions are wrong, the model may produce incorrect predictions. Determining the best distribution to utilize could be challenging in some circumstances.
- Susceptible to Overfitting: Probabilistic models are susceptible to overfitting, which occurs when a model is overly complicated and catches noise in the data rather than underlying patterns. Overfitting can affect other machine learning models as well as probabilistic methods. When using the model on fresh data, this may result in poor generalization
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Conclusion
Machine learning is not possible without the use of probability theory, which is a potent tool for modeling complicated, uncertain systems. We can define data uncertainty and generate predictions based on it using probability-based machine learning algorithms. The common approaches used in probability-based machine learning models include the Bayes theorem, maximum likelihood estimates, Markov chain Monte Carlo methods, and Gaussian processes. These models have numerous uses in a variety of fields, including manufacturing, finance, and the medical and healthcare fields. Probability theory will stay essential in helping machines learn from data and make wise decisions as machine learning technology advances. To learn more about Machine Learning, check out this interesting and knowledge driven PG course of Great Learning.
