Showing posts with label Computational analysis pipelines. Show all posts
Showing posts with label Computational analysis pipelines. Show all posts

Artificial Intelligence - What Is Automated Machine Learning?



Machine learning algorithms are created with the goal of detecting and describing complex patterns in massive datasets.

By taking the uncertainty out of constructing instruments of convenience, automated machine learning (AutoML) aims to deliver these analytical tools to everyone interested in large data research.

"Computational analysis pipelines" is the name given to these instruments.

While there is still a lot of work to be done in automated machine learning, early achievements show that it will be an important tool in the arsenal of computer and data scientists.

It will be critical to customize these software packages to beginner users, enabling them to undertake difficult machine learning activities in a user-friendly way while still allowing for the integration of domain-specific knowledge and model interpretation and action.

These latter objectives have received less attention, but they will need to be addressed in future study before AutoML is able to tackle complicated real-world situations.

Automated machine learning is a relatively young field of research that has risen in popularity in the past ten years as a consequence of the widespread availability of strong open-source machine learning frameworks and high-performance computers.

AutoML software packages are currently available in both open-source and commercial versions.

Many of these packages allow for the exploration of machine learning pipelines, which can include feature transformation algorithms like discretization (which converts continuous equations, functions, models, and variables into discrete equations, functions, and so on for digital computers), feature engineering algorithms like principal components analysis (which removes large dimensions of "less important" data while keeping a subset of "more important" variables), and so on.

Bayesian optimization, ensemble techniques, and genetic programming are examples of stochastic search strategies utilized in AutoML.

Stochastic search techniques may be used to solve deterministic issues that have random noise or deterministic problems that have randomness injected into them.

New methods for extracting "signal from noise" in datasets, as well as finding insights and making predictions, are currently being developed and tested.

One of the difficulties with machine learning is that each algorithm examines data in a unique manner.

That is, each algorithm recognizes and classifies various patterns.

Linear support vector machines and k-nearest neighbor algorithms are excellent at detecting linear patterns, whereas k-nearest neighbor methods are effective at detecting nonlinear patterns.

The problem is that scientists don't know which algorithm(s) to employ when they start their job since they don't know what patterns they're looking for in the data.

The majority of users select an algorithm that they are acquainted with or that seems to operate well across a variety of datasets.

Some people may choose an algorithm because the models it generates are simple to compare.

There are a variety of reasons why various algorithms are used for data analysis.

Nonetheless, the approach selected may not be optimal for a particular data set.

This task is especially tough for a new user who may not be aware of the strengths and disadvantages of each algorithm.

A grid search is one way to address this issue.

Multiple machine learning algorithms and parameter settings are applied to a dataset in a systematic manner, with the results compared to determine which approach is the best.

This is a frequent strategy that may provide positive outcomes.

The grid search's drawback is that it may be computationally demanding when a large number of methods, each with several parameter values, need to be examined.

Random forests are classification algorithms comprised of numerous decision trees with a number of regularly used parameters that must be fine-tuned for best results on a specific dataset.

The accepted machine learning approach adjusts the data using parameters, which are configuration variables.

The maximum number of characteristics that may be used in the decision trees that are constructed and assessed is a typical parameter.

Automated machine learning may aid in the management of the complicated, computationally costly combinatorial explosion that occurs during the execution of specialized investigations.

A single parameter might have 10 distinct configurations, for example.

Another parameter might be the number of decision trees to be included in the forest, which could be 10 in total.

Another ten possible parameters might be the minimum amount of samples that would be permitted in the "leaves" of the decision trees.

Based on the examination of just three parameters, this example gives 1000 distinct alternative parameter configurations.

A data scientist looking at ten different machine learning methods, each with 1000 different parameter values, would have to undertake 10,000 different studies.

Hyperparameters, which are characteristics of the analyses that are established ahead of time and hence not learnt from the data, are added on top of these studies.

They are often established by the data scientist using a variety of rules of thumb or values derived from previous challenges.

Comparisons of numerous alternative cross-validation procedures or the influence of sample size on findings are examples of hyperparameter setups.

Hundreds of hyperparameter combinations may need to be assessed in a typical case.

The data scientist would have to execute a total of one million analyses using a mix of machine learning algorithms, parameter settings, and hyperparameter settings.

Given the computer resources available to the user, so many distinct studies might be prohibitive depending on the sample size of the data to be examined, the number of features, and the kinds of machine learning algorithms used.

Using a stochastic search to approximate the optimum mix of machine learning algorithms, parameter settings, and hyperparameter settings is an alternate technique.

Until a computational limit is reached, a random number generator is employed to sample from all potential possibilities.

Before making a final decision, the user manually explores various parameter and hyperparameter settings around the optimal technique.

This has the virtue of being computationally controllable, but it has the disadvantage of being stochastic, since chance may not explore the best combinations.

To address this, a stochastic search algorithm with a heuristic element—a practical technique, guide, or rule—may be created that can adaptively explore algorithms and settings while improving over time.

Because they automate the search for optimum machine learning algorithms and parameters, approaches that combine stochastic searches with heuristics are referred to as automated machine learning.

A stochastic search could begin by creating a variety of machine learning algorithm, parameter setting, and hyperparameter setting combinations at random and then evaluate each one using cross-validation, a method for evaluating the effectiveness of a machine learning model.

The best of these is chosen, modified at random, and assessed once again.

This procedure is continued until a computational limit or a performance goal has been met.

This stochastic search is guided by the heuristic algorithm.

Optimal search strategy development is a hot topic in academia right now.

There are various benefits to using AutoML.

To begin with, it has the potential to be more computationally efficient than the exhaustive grid search method.

Second, it makes machine learning more accessible by removing some of the guesswork involved in choosing the best machine learning algorithm and its many parameters for a particular dataset.

This allows even the most inexperienced user to benefit from machine learning.

Third, if generalizability measurements are included into the heuristic being utilized, it may provide more repeatable outcomes.

Fourth, including complexity metrics into the heuristic might result in more understandable outcomes.

Fifth, if expert knowledge is included into the heuristic, it may produce more actionable findings.

AutoML techniques do, however, present certain difficulties.

The first is the risk of overfitting, which occurs when numerous distinct methods are evaluated, resulting in an analysis that matches existing data too closely but does not fit or forecast unknown or fresh data.

The more analytical techniques used on a dataset, the more likely it is to learn the data's noise, resulting in a model that is hard to generalize to new data.

With any AutoML technique, this must be thoroughly handled.

Second, AutoML is computationally demanding in and of itself.

Third, AutoML approaches may create very complicated pipelines including several machine learning algorithms.

This may make interpretation considerably more challenging than just selecting a single analytic method.

Fourth, this is a very new field.

Despite some promising early instances, ideal AutoML solutions may not have yet been devised.

~ Jai Krishna Ponnappan

You may also want to read more about Artificial Intelligence here.

See also: Deep Learning.

Further Reading

Feurer, Matthias, Aaron Klein, Katharina Eggensperger, Jost Springenberg, Manuel Blum, and Frank Hutter. 2015. “Efficient and Robust Automated Machine Learning.” In Advances in Neural Information Processing Systems, 28. Montreal, Canada: Neural Information Processing Systems.

Hutter, Frank Hutter, Lars Kotthoff, and Joaquin Vanschoren, eds. 2019. Automated Machine Learning: Methods, Systems, Challenges. New York: Springer.

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