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Feature vectors represent features used by machine learning models in multi-dimensional numerical values. As machine learning models can only deal with numerical values, converting any necessary features into feature vectors is crucial. Here, we discuss feature vectors in various use cases. We also explain the difficulties in generating and managing feature vectors.
In this post, you will learn about:
A feature vector is an ordered list of numerical properties of observed phenomena. It represents input features to a machine learning model that makes a prediction.
Humans can analyze qualitative data to make a decision. For example, we see the cloudy sky, feel the damp breeze, and decide to take an umbrella when going outside. Our five senses can transform outside stimuli into neural activity in our brains, handling multiple inputs as they occur in no particular order.
However, machine learning models can only deal with quantitative data. As such, we must always convert features of observed phenomena into numerical values and feed them into a machine learning model in the same order. In short, we must represent features in feature vectors.
There are different types of features and techniques that are useful for building a feature vector, including:
In exploratory data analysis, researchers try to discover features from raw data. They may start with qualitative research, looking at visualizations and applying their domain expertise to deduce an idea that can transform the observation into feature vectors. For example, a feature vector in data mining represents a hidden pattern in large data sets, such as equity trading buy/sell signals from the historical trading price and volume data.
In the field of natural language processing, the process of splitting sentences into distinct entities is known as tokenization. For instance, researchers could treat each word or phoneme as a unique token to generate feature vectors for further analysis and experiments.
In computer vision, the RGB color scheme isn’t the only way to represent image pixels. For example, there is also HSL (hue, saturation, lightness) and HSV (hue, saturation, value). Sometimes, practitioners even use a monochrome scheme to reduce noises originating from color images.
Ultimately, researchers explore different feature vectors to evaluate the performance of their predictive models. Once the feature design is ready, they are good to go to the next stage.
Feature engineering is, in large part, the systematic process of generating feature vectors from raw data. There are, however, some obstacles to setting up such a process. First, we need a place to store generated feature vectors for later retrieval. We also need to update feature definitions from time to time to accommodate changes in the underlying dynamics or the latest discovery.
In other words, we must keep features up to date, as they change over time. However, applications cannot jump from an old feature definition to a new one overnight, so we also need to keep track of multiple versions of feature definitions. This complicates the management of feature vectors. Moreover, various teams need to share feature vectors, even though they are in different AI product development stages.
The Iguazio MLOps platform’s feature store simplifies feature engineering for both batch and real-time processing. It stores and monitors feature vectors, making them available with versions and accessible through various API calls. This ensures easy management and transitions for applications to new feature definitions over time.