blog posts

What Is Automatic Machine Learning And Why Might It Be Transformative?

Researchers And Developers Use Machine Learning In A Variety Of Ways To Model Intelligent Algorithms, Each With Its Own Advantages.

Various paradigms such as machine learning with supervision, without supervision, reinforcement, and online have been made available to experts after years of effort and research in a specific framework. Still, a relatively emerging phenomenon that may play a major role in this area in the future is automatic machine learning.

Which has more efficient applications. AutoML refers to a unique way of building an automated machine learning algorithm. At first glance, it seems that automatic machine learning is supposed to replace scientists, but the applications of automatic learning are far beyond scientists’ expertise.

How are smart models made?

Machine learning is one of the important applications of artificial intelligence that enables automatic and advanced experience-based learning without explicit programming for a system. More specifically, machine learning focuses on developing computer programs that access data and use it for self-paced learning. A machine learning model is based on a regular and specific program as follows:

  •    Finding a problem with your business.
  •     Translating a business problem into a problem that can be solved by data science.
  •      Find the required data set.
  •    Define the purpose and criteria needed to estimate.
Build and teach models based on feature engineering, feature selection, algorithm selection, hyperparameter optimization (in automatic machine learning, hyperparameters are measures or metrics that control the learning process.

Hyperparameter optimization, known as tuning), is the process of tuning. An intelligent algorithm’s parameters are stacking requirements, aggregation, model deployment, evaluation and testing, model commercialization, and finally, model application to solve real problems.

Automated machine learning implements a complete gateway to machine learning processes ranging from raw data sets to usable model building. Due to the automation of AutoML, even non-experts can use machine learning models and techniques without the need for specialized knowledge.

 What does AutoML seek to automate?

In conventional machine learning, a set of input data is provided to the algorithm for learning. This raw data may not prepare in the appropriate format used to teach an algorithm or similar algorithms.

For data to be used for machine learning, an expert must focus on data overfitting, feature engineering, feature extraction, and select features. After this step, the algorithm should select the parameters optimized to maximize the model’s predictive performance.

All of these steps have challenges, and AutoML tries to simplify these steps considerably for non-experts. AutoML was developed to simplify and expedite these processes. However, automatic machine learning also poses new challenges.

The first challenge is how to ensure the output of a model based on the AutoML approach, is this model really the best model to be built? This is why most AutoML technologies and solutions provide the ability to edit some hyperparameters so that developers can create different models and compare them.

Of course, the above solution also has disadvantages. While editing and manipulating super parameters give developers good capabilities, it eliminates the advantages of automated machine learning: simplicity and speed.

The second problem with AutoML-related technologies is their heavy reliance on technical knowledge.

More specifically, AutoML requires a data scientist to examine the designed model and select the most appropriate model to implement. If several well-functioning models are available during the evaluation phase, the data scientist should use other complementary technologies to build the final model that can commercialize.

This eliminates the inherent advantages of AutoML, which is the ease and speed of the learning debate. Researchers have suggested ways to overcome these problems. One way to overcome this problem is to implement AutoML at different levels.

Technically, the difference between automatic machine learning and traditional machine learning is in some previously fixed and unchangeable parameters, but now it is possible to edit them.

In other respects, there is no difference between the two paradigms. Initially, AutoML can perform a comprehensive search on available hyperparameters and models. Do you think this sentence is familiar to you? You are right because we are talking about a concept called pervasive search.

Yes, AutoML does the same thing as a comprehensive search. Since designing the best custom model for a particular application is time-consuming and costly, automatic machine learning allows us to do more experiments.

These experiments can give us ideas about what works well and what does not.

The fact is that the implementation of today’s machine learning models is based more on trial and error, and not all components can assure to work properly.

Models should be evaluated by various tests and the results evaluated to identify and correct errors. If the cost of implementing each of the tests is high, many calculations must do to select the most appropriate option.

What is overfitting?

Overfitting is an undesirable phenomenon in statistical discussions and machine learning. To deal with this phenomenon and select the appropriate degree of freedom, methods such as cross-validation and regulation should use.

Overfitting occurs because the model fit criterion is not the same as the criterion used to evaluate it. Overall, overfitting occurs when the model starts memorizing data instead of learning it during fitting.

What is the purpose of using automatic machine learning?

Automated machine learning can use in various fields related to the design of intelligent algorithms. These areas include data preparation, column type recognition (logical, discrete numbers, continuous, or textual), the reasoning for column construction (data tag, classification, numerical properties, textural properties), task recognition (binary classification, Regression, classification, feature engineering, feature selection, feature extraction, meta-learning and transfer learning, detection, and correction of embedded / wrong or missing values, model selection, optimization of learning algorithm super parameters, data transfer bus selection based on time constraints, Memory and complexity constraints, selection of evaluation criteria and validation methods, troubleshooting, leakage detection, misconfiguration detection, analysis of results and imaging.

What are the stages of automatic machine learning?

In general, the automated machine learning process can be divided into three main parts: data preparation, feature engineering, creation, and model performance estimation.

1. Data preparation

Without data, it is not possible to build any intelligent models or algorithms. Therefore, researchers should seek to collect data and prepare data. The data preparation step includes the following subsets:

Collecting data

Researchers need large amounts of data to prepare models and conduct extensive research to provide the feed needed for intelligent models. That’s why useful data sets are available to developers for free. In the early stages of machine learning development, datasets were developed using handwritten numbers, and then big data such as CIFAR-10, CIFAR-100, and ImageNet develop.

Various big data such as Kaggle, Google Dataset Search, and Elsevier Data prepared for this purpose were made available to developers on June 3, 2020, by Wayback Machine. However, These methods are not available. Two solutions are suggested to solve this problem:

Data search

By searching the web, which is an endless source of data, it is possible to obtain some data, but this method has many problems, such as invalid data or no data tag. To solve these problems, researchers have developed methods such as automatic data tagging.

Data simulation

Data simulation is one of the most widely used methods for generating data. The technology works in such a way that researchers use simulators that are as realistic as possible. OpenAI Gym is a popular tool for creating various simulation environments. The second method is to generate data using Generative Adversarial Networks.

Data refining

The collected data is subject to a lot of confusion and heterogeneity so that noise can harm model training, so the data cleaning process must be done. Data cleansing typically requires a small number of professionals who are highly paid to do so. That’s why methods and systems like BoostClean invent to automate this process. Of course, these systems can only work on fixed data sets and therefore are not responsive in the real world where a lot of data is generated daily.

Data redundancy

Data Augmentation refers to the fact that new data can generate based on existing data, and, to some extent, data collection tools can use for this purpose. From the above approach to regularization and dealing with the problem

Over-fit training data is used. For example, in machine vision, it is possible to use the data redundancy mechanism about image data and enlarge or reduce the image or cut parts of it to create new images. AutoAugment was one of the first systems to be implemented based on this technique.

The data overclocking process has not been fully and accurately implemented by any AutoML tools. More work needs to be done to provide the developers with an accurate tool.

2. Properties Engineering

The goal of Feature Engineering is to maximize the features extracted from raw data for use by algorithms and models. This section includes other subcategories as follows:

  • Selected features: the choice of features (Feature Selection) is a subset of the original collection features. The elimination of irrelevant or repetitive characters is selected. This is done to simplify the model design process so that there is no need for over-loading and the model performance is improved. Among the methods available in this field are the first level search, the first best, simulated annealing, and genetic algorithms.
  • Feature Construction: In the Feature Construction process, new properties are created from raw data to enhance the robustness of the model. For example, methods such as supplementing logical properties and minimizing or maximizing numerical data are used.
  • Interior features feature extraction (Feature Extraction) solution for reduced dimensions. This process relies on mapping functions to extract information and non-repetitive properties based on specific metrics. Feature extraction is based on the correlation function, which can implement in a manner such as PCA. However, some researchers believe that feed-forward neural networks can use for this purpose.

3. Build and estimate model performance

Model construction is divided into two parts: search space and optimization methods. In the search space, the structure on which the model is built is defined. The models are generally divided into three groups: common machine learning (SVM) models, k-nearest neighbors algorithm, and deep neural network (DNN) decision tree, each with its own definitions.

How can automated machine learning be implemented?

Today, various solutions are available to researchers to automate the process of building machine learning models. Simple and basic methods of random search and lattice search have wide applications in this field because they are easy to implement.

But these solutions do not work best. In fact, the performance quality of these search methods is constant, and learning will not be based on past results, so we will not see an improvement in the performance level of models based on this method over time…

Today, new meta-learning solutions have better results than the old ones.

Among the new solutions that allow the implementation of automated machine learning are evolutionary algorithms, gradient-based optimization, and Bayesian optimization.