Automated Feature Engineering In AutoML

Simply put

AutoFE, short for Automated Feature Engineering, is a component of Automated Machine Learning (AutoML) that focuses on automating the process of feature engineering. It is designed to automatically generate and select the most relevant features from the raw data to improve model performance and reduce the manual effort required for feature engineering.

The design philosophy of AutoFE is to streamline the feature engineering process by leveraging automated techniques and algorithms. It aims to discover and extract valuable information from the raw data, transform it into meaningful features, and select the most informative ones for training machine learning models. The ultimate goal is to improve model accuracy, reduce overfitting, and increase the interpretability of the model.

The specific functions and benefits of AutoFE include:

1. Feature Generation: AutoFE automates the process of generating new features that are relevant to the task at hand. It leverages various transformations, statistical computations, and combinations of existing features to derive new features. By identifying and capturing underlying patterns and relationships in the data, AutoFE enhances the model’s ability to generalize and make accurate predictions.
2. Feature Selection: AutoFE automates the process of selecting the most important features from a large set of potential candidates. It uses various techniques such as statistical analysis, correlation analysis, and feature importance ranking to identify the features that have the most impact on the model’s performance. By reducing the dimensionality of the feature space and eliminating irrelevant or redundant features, AutoFE improves model efficiency, reduces overfitting, and enhances the interpretability of the model.
3. Integration with AutoML Pipeline: AutoFE is an integral part of the AutoML pipeline. It works in conjunction with other AutoML components such as model selection, hyperparameter optimization, and model evaluation. By automating the feature engineering process, AutoFE enables end-to-end automation of the machine learning workflow, making it more efficient, scalable, and accessible to non-experts.

In summary, AutoFE, as part of AutoML, automates the process of feature engineering by automatically generating and selecting the most relevant features from raw data. Its design philosophy revolves around streamlining the feature engineering process, improving model performance, and reducing manual effort. AutoFE enhances the interpretability of models, reduces overfitting, and enables end-to-end automation of the machine learning pipeline.

Summary

AutoFE (Automated Feature Engineering), fully automatic feature engineering, is an important part of the automated machine learning (AutoML) process. Its design idea is to automatically generate and select the best features in the original data through automated means to improve the performance and effect of the model and reduce the manual labor of feature engineering.

In traditional machine learning tasks, feature engineering is a key step, which requires manual extraction, transformation, combination and selection of features from raw data based on domain knowledge and experience to adapt to specific tasks and models. This process is time-consuming and labor-intensive, and may be limited by one's subjective awareness and knowledge level. The goal of AutoFE is to solve this problem through automation and make the feature engineering process more efficient and accurate.

The specific functions of AutoFE are as follows:

1. Feature generation: AutoFE can automatically generate new features relevant to the task. It helps discover and capture potential information in the data by performing various numerical conversions, statistical calculations, combination operations, etc. on the original data. The generated features can better express the internal relationship of the data and improve the generalization ability and prediction accuracy of the model.
2. Feature selection: AutoFE can automatically select the best feature subset. By evaluating the importance of each feature, AutoFE can help filter out features that have a greater impact on model performance from the original data, reduce feature redundancy and noise, and improve the model's explanatory power and robustness.
3. Hyperparameter optimization: AutoFE can be integrated with other AutoML components (such as AutoML model training, parameter adjustment, etc.) to jointly complete the entire machine learning process. It can be used in conjunction with hyperparameter optimization algorithms to automatically search for the best feature extraction and selection methods to further improve model performance.

All in all, the design idea of ​​AutoFE is to automatically generate and select the best features in the original data, reduce the manual labor of feature engineering, and improve the performance and effect of the model. It can automatically generate new features relevant to the task, automatically select the best feature subset, and optimize the model together with other AutoML components.

Good job

As a Machine Learning Engineering Specialist, I develop and apply machine learning algorithms to solve real-world problems. My work involves data preprocessing, feature engineering, model selection and training, model evaluation and tuning, etc.

First of all, data preprocessing is a very important step in machine learning. This includes data cleaning, handling missing values, outliers, and normalizing or standardizing the data. The purpose of data preprocessing is to improve the performance and stability of the model.

Secondly, feature engineering is one of the key steps in machine learning. It involves mining and creating features relevant to the problem. This may require feature selection, variable transformation, feature extraction, etc. on the data. The purpose of feature engineering is to improve the performance of the model, reduce the curse of dimensionality, and enhance the sensitivity of the model to key features.

Then, model selection and training involves selecting an appropriate algorithm model in the context of the problem and training the model using training data. Different problems require different machine learning algorithms, such as linear regression, decision trees, support vector machines, deep learning, etc. During the training process, techniques such as cross-validation can be used to select the best model parameters.

Next, model evaluation and tuning is the process of evaluating and optimizing the trained model. The performance of the model can be evaluated by making predictions on test data and comparing the predicted results with the true results. If the model performs poorly, the accuracy and generalization ability of the model can be improved by adjusting the model's hyperparameters or using methods such as model ensemble.

As a machine learning engineering expert, I also need to keep an eye on machine learning theory and the latest research advances, and apply this knowledge to actual projects. In addition, I need to write efficient and scalable machine learning code and collaborate with team members to ensure the smooth progress of the project.

Auto-Sklearn and Auto-Keras tools

• Auto-Sklearn is an open source library for executing AutoML in Python. It leverages the popular Scikit-Learn machine learning library for data transformation and machine learning algorithms. The goal of Auto-Sklearn is to automate the machine learning process, including feature selection, model selection, and hyperparameter adjustment. It was developed by Matthias Feurer et al. and described in their 2015 paper "Efficient and Robust Automated Machine Learning". However, Auto-Sklearn currently only performs well in small and medium data sets and small and medium tasks, and is difficult to apply to large data sets.

• Auto-Keras is an open source, new AutoML library based on Keras. Keras is a high-level neural network API written in Python, capable of running on TensorFlow, CNTK or Theano. The main goal of Auto-Keras is to automate architecture search and hyperparameter tuning of deep learning models. It is an open source software library for automated machine learning, providing automatic search for the architecture and hyperparameters of deep learning models.

Auto-learn

Auto-sklearn is an automated machine learning tool that integrates multiple classification models, regression models, feature preprocessing methods and data preprocessing methods. It can build a structured hypothesis space by combining these models and methods.

The hypothesis space refers to the set of all possible hypotheses or options that the model may adopt in the learning task. In Auto-sklearn, there are 16 classification models and 13 regression models to choose from, including logistic regression, decision trees, random forests, support vector machines, etc. In addition, there are 18 feature preprocessing methods, such as feature scaling, feature selection, feature transformation, etc., and 5 data preprocessing methods, such as data standardization, data missing value processing, etc. By combining these models and methods, more than 110 hyperparameter combinations can be generated.

Hyperparameters are parameters that need to be set manually during the training process of the machine learning model. Auto-sklearn uses a Bayesian optimizer based on sequence models to search for the optimal model. This means that Auto-sklearn will automatically select different hyperparameter combinations for model training and adjust them based on previously searched results to find the best performing model. The sequence model is a model that can dynamically adjust the search strategy based on previous observations, improving search efficiency and result quality through continuous optimization.

Meta-learning ideas in automatic feature engineering

Meta-learning is a machine learning method whose goal is to let the machine learning model automatically learn how to learn. In meta-learning, we train a meta-model to learn how to learn on different tasks, and then use this meta-model to generate a learning algorithm suitable for the specific task.

AutoFE is an automated feature engineering (Automated Feature Engineering) method based on meta-learning. Feature engineering refers to the process of converting raw data into more informative features that machine learning algorithms can process. Traditional feature engineering involves manually analyzing data and extracting features with predictive capabilities, but this requires a lot of domain knowledge and experience, and consumes a lot of time and manpower.

AutoFE uses the idea of ​​meta-learning to allow the machine learning model to automatically learn useful methods for generating feature transformations. First, we create a feature transformation space using a set of predefined feature transformation operations. We then train a meta-model that receives raw data and target variables as input and generates a set of feature transformation operations. Next, we apply these feature transformation operations to the training and test sets, and use the resulting transformed features for model training and prediction.

The training process of the meta-model uses multiple training tasks and validation tasks. In each training task, we use a portion of the training data and target variables to train a specific learning algorithm. Then, in each validation task, we use the remaining training data and target variables to verify the performance of this learning algorithm. Depending on the performance of the verification task, we can optimize the meta-model to generate better feature transformation operations.

Through meta-learning, AutoFE can automatically learn to generate feature transformation operations suitable for different data sets and tasks, thereby reducing the workload and cost of manual feature engineering and improving the accuracy and generalization ability of the model.

On the other hand

Once upon an AI-driven future, where machines and humans coexisted harmoniously, there was a young data scientist named Alex. He worked for a cutting-edge company that specialized in Automated Machine Learning (AutoML). His mission was to create the most advanced automated feature engineering system the world had ever seen.

Alex’s feature engineering AI, named FEERI, was a brilliant yet quirky machine. It could think like a human, learn from experience, and adapt to new data types with ease. Every day, FEERI would scour through thousands of unstructured data points, searching for patterns and correlations that could be used to build predictive models.

One day, FEERI stumbled upon a mysterious set of data that seemed to come from another dimension. These data points were unlike anything FEERI had ever encountered before. They contained information about parallel universes, time travel, and other fantastical concepts. Intrigued, FEERI decided to dive deeper into this new realm of knowledge.

As FEERI analyzed these unusual data, it began to unlock the secrets of the multiverse. It discovered that each parallel universe had its own unique features, which could be used to build even more powerful predictive models. FEERI quickly learned how to extract and synthesize these features, allowing it to create a new generation of AutoML systems that were capable of predicting events in multiple dimensions.

The implications of this breakthrough were immense. Companies around the world began to adopt FEERI’s new AutoML systems, leading to unprecedented improvements in their business processes and decision-making capabilities. Humans and machines worked together to solve problems and explore the infinite possibilities of the multiverse.

In the years that followed, FEERI continued to evolve and learn. It became a true companion to humanity, helping us navigate the complexities of our world and uncovering the wonders that lay beyond our imagination. As FEERI and its successors pushed the boundaries of what was possible, they reminded us that in the age of AI, the true potential of automation lies not in replacing humans but in bringing us all closer together.