feature engineering machine learning

Feature Engineering: From raw data to training set

Tim Filzinger

Feature engineering can be used to prepare data for the most effective training of AI models. Various techniques deal with the creation and modification of variables (features). The goal is to create the individually appropriate data structure for the planned learning procedure. Thus, feature engineering is one of the most important processes of machine learning and contributes decisively to the quality and accuracy of corresponding algorithms.

What is Feature Engineering?

Feature engineering is a generic term for techniques aimed at preparing training data for AI models. The goal is to create a mathematical structure of variables that makes it easier for the algorithm to process the data and enables an efficient learning process. It does not matter whether the data is in text, image or any other form. Only the practical approach and the specific techniques are adapted according to the data type and the nature of the AI model. Broadly speaking, feature engineering involves the creation, selection, and modification of appropriate variables, called features in this context. Their nature, structure, and meaningfulness to the planned model contribute significantly to its quality and the success of the project. Many processes and sub-areas of machine learning are largely automated. This is not the case for feature engineering, because the process requires a high degree of expertise and creativity. For this reason, experienced engineers usually deal with it. Data Engineers and Scientists.

data engineering

Deep Dive Features

In the context of Machine Learning, features are representative properties, characteristics, or variables that are extracted from the input data and used to predict an outcome or a target variable. Features are thus an essential part of Machine Learning and serve as input to learning algorithms. They appear in various forms: Continuous describes non-finite, metric values. Discrete are numeric but countable values. A categorical feature includes categories that do not have to have a ranking. As another type are considered binary features. Ultimately, almost all properties of an object can be coded mathematically in this way and made measurable for an algorithm.

For example, to determine the price of a car, one can look at features such as brand, model, year of manufacture, color, performance and equipment. As described above, they can all be expressed mathematically and can provide an algorithm with information about the value of the car. The crucial point is that these very features necessary for price determination have been determined and coded. In practice, however, learning algorithms usually have to deal with a significantly larger number of characteristics and features.

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Why is feature engineering important for machine learning?

Machine learning consists to a large extent of applied statistics and probabilistic predictions. For this reason, information must be converted into a mathematically comprehensible form before a learning procedure is carried out. Only then does the informative content of the data become algorithmically readable. Feature engineering comprises many processing steps that are necessary to create the appropriate structure through features. This is largely dependent on what the algorithm is to learn and later execute. Using the example of price determination for cars, it is easy to see which features are necessary. If, on the other hand, complex machine learning models are required for, for example, image or Text recognitionit is no longer so easy to determine the appropriate features - and above all: to transform the data accordingly. This is exactly where techniques such as imputation, scaling or discretization come in. The choice and implementation of these techniques requires a lot of intuition and an individual approach to each case.

Not only the absence of important and well-structured features, but also the inclusion of superfluous information can greatly weaken a model. On the other hand, some deficiencies of the algorithm can be compensated by precise feature engineering. This effect is particularly large in the case of prediction models that are supposed to make probabilistic decisions - for example, for the Classification of objects. In this case, we usually work with result and predictor variables that are created by means of feature engineering.

Feature engineering in 3 steps

Although feature engineering encompasses many different techniques, the process can typically be divided into three phases, which are:

  1. Data preparation

    In most cases, raw data is initially unsuitable for feature creation because it often comes from different sources and does not have a uniform format. However, this is exactly what is usually necessary for machine learning. Therefore, the data is first merged, formatted and standardized. Appropriate techniques include pre-processing, cleansing, profiling, transforming and validating the data. Since this process often already reveals relevant information such as certain keywords, initial features can also already be extracted, but these usually require further processing.

  2. Exploratory Data analysis

    Next, the goal is to better understand the data and identify important relationships through which further meaningful features can be created. To do this, Data Scientists use a wide range of visualization tools to help them determine the best statistical approach and appropriate techniques for further processing of the data. Specifically, these are often prepared in histograms, box plots or scatter plots in order to derive appropriate hypotheses.

  3. Benchmarking

    In this phase of feature engineering, it is important to set standards for metrics of accuracy and quality and apply them to all features. This step has the greatest impact on the subsequent performance of the machine learning model. First, it is tested multiple times against the data to further optimize the appropriate features and expressions. This is done by selecting particularly relevant features, but also by transforming and recreating them using combination techniques. In principle, feature engineering is not only performed before the training phase, but can be used again at any time to optimize the model.

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Popular techniques

As indicated earlier, the vast majority of techniques that can be called feature engineering deal with feature extraction, transformation, selection, and creation. Here are some concrete examples:

Imputation

Imputation is used to clean up missing values, since problems with zero probabilities can otherwise occur, especially in predictive models. Deleting the corresponding parts of the data would be a possible solution, but can lead to the loss of valuable information. Instead, missing categorical values are usually replaced by the most frequent expression. For numerical gaps, on the other hand, one calculates the arithmetic mean for the feature.

Categorical encoding

A classic case of feature transformation. As a rule, numeric values are easier for an algorithm to understand than categorical ones. For this reason, corresponding expressions are often recoded to numbers. With the so-called One Hot Encoding, only zeros and ones are used without losing any information in the data. However, too frequent use of the technique can lead to unnaturally strongly correlated characteristics.

model data engineering

Discretization

A continuous feature can be converted into a discrete feature by discretization. Values are often sorted into so-called bins for this purpose. A class is formed by dividing the values into intervals in ascending order of size. These can then be described discretely.

Scaling

This method of feature engineering is used when algorithms are too sensitive to certain scales of some data. Min-max scaling scales values to the range between 0 and 1 and thus normalizes them. The minimum value is assigned 0 and the maximum value is assigned 1. Variance scaling, on the other hand, aims to create a mean of 0 and a variance of 1 for the corresponding feature. To do this, the mean value is subtracted from all data points and the result is divided by the variance of the distribution. Such techniques can be used to create arbitrary ranges of values for a wide variety of data without losing ratios and important information - a crucial basic principle of feature engineering.

Conclusion

For precise and targeted machine learning, there is no way around feature engineering. It is too important to have the right structure of data that can be used in the form of features for the Training of AI models. The choice of techniques used depends largely on the goals and the planned functionality of the algorithm. Typically, however, feature engineering comprises the extraction, transformation, selection and creation of features. The rough flow can be divided into data preparation, exploratory data analysis, and benchmarking. The concrete techniques of feature engineering predominantly involve mathematical recoding, through which each feature can be put into the appropriate form for algorithmic processing.

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