The Role of Feature Extraction in Machine Learning

To more accurately make predictions and recommendations, machine learning involves massive data sets that demand significant resources to process. Feature extraction is an effective method used to reduce the amount of resources needed without losing vital information. Feature extraction plays a key role in improving the efficiency and accuracy of machine learning models. 

Feature Engineering, Feature Extraction, and Feature Selection

Features are variables that can be defined and observed. For example, in a healthcare context, features may include gender, height, weight, resting heart rate, or blood sugar levels. 

Feature engineering is the process of reworking a data set to improve the training of a machine learning model. By adding, deleting, combining, or mutating the data within the data set, data scientists expertly tailor the training data to ensure the resulting machine learning model will fulfill its business use case. Data scientists use feature engineering to prepare an input data set that’s best suited to support the intended business purpose of the machine learning algorithm. For example, one method involves handling outliers. Since outliers fall so far out of the expected range, they can negatively impact the accuracy of predictions. One common way of dealing with outliers is trimming. Trimming simply removes the outlier values, ensuring they don’t contaminate the training data. 

Feature extraction is a subset of feature engineering. Data scientists turn to feature extraction when the data in its raw form is unusable. Feature extraction transforms raw data, with image files being a typical use case, into numerical features that are compatible with machine learning algorithms. Data scientists can create new features suitable for machine learning applications by extracting the shape of an object or the redness value in images.

Feature selection is closely related. Where feature extraction and feature engineering involve creating new features, feature selection is the process of choosing which features are most likely to enhance the quality of your prediction variable or output. By only selecting the most relevant features, feature selection creates simpler, more easily understood machine learning models. 

Feature Extraction Makes Machine Learning More Efficient

Feature extraction improves the efficiency and accuracy of machine learning. Here are four ways feature extraction enables machine learning models to better serve their intended purpose:  

Reduces redundant data

Feature extraction cuts through the noise, removing redundant and unnecessary data. This frees machine learning programs to focus on the most relevant data. 

Improves model accuracy 

The most accurate machine learning models are those developed using only the data required to train the model to its intended business use. Including peripheral data negatively impacts the model’s accuracy.

Boosts speed of learning

Including training data that doesn’t directly contribute to solving the business problem bogs down the learning process. Models trained on highly relevant data learn more quickly and make more accurate predictions. 

More-efficient use of compute resources

Pruning out peripheral data boosts speed and efficiency. With less data to sift through, compute resources aren’t dedicated to processing tasks that aren’t generating additional value.

Feature Extraction Techniques

Data scientists use many feature extraction methods to tap into the value of raw data sources. Let’s look at three of the most common and how they’re used to extract data useful for machine learning applications. 

Image processing

Feature extraction plays an important role in image processing. This technique is used to detect features in digital images such as edges, shapes, or motion. Once these are identified, the data can be processed to perform various tasks related to analyzing an image. 

Bag of words

Used in natural language processing, this process extracts words from text-based sources such as web pages, documents, and social media posts and classifies them by frequency of use. The bag-of-words technique supports the technology that enables computers to understand, analyze, and generate human language.

Autoencoders

Autoencoders are a form of unsupervised learning designed to reduce the noise present in data. In autoencoding, input data is compressed, encoded, and then reconstructed as an output. This process leverages feature extraction to reduce the dimensionality of data, making it easier to focus on only the most important parts of the input.

Roadblocks to Efficient Feature Extraction

Machine learning is a powerful technology, but many organizations have yet to implement it due to significant challenges. 

Poorly designed data pipelines

Data preparation is one of the most important parts of the machine learning process. If poor-quality data is input, the output quality will match. Poorly designed or overly complicated machine learning data pipelines can stifle innovation and are costly to create and maintain. 

Compute resource contention 

Machine learning programs consume significant computing resources. Organizations without scalable compute resources may find it difficult to dedicate the resources required for maintaining a robust machine learning program while still maintaining day-to-day business operations.

Siloed data 

Machine learning models require massive amounts of data to train and deploy. But many organizations have their data spread over multiple systems, often in different formats. Without a single source of truth to draw from, it’s difficult to gain a complete view across the entire business.

Not fully utilizing AutoML

As its name implies, automated machine learning automates much of the machine learning process. Automated machine learning (AutoML) speeds up tasks and eliminates the need to manually complete time-consuming processes, freeing machine learning experts to focus on higher-level tasks.

Snowflake for Machine Learning

With Snowflake, data engineers and data scientists can perform machine learning workloads on large, petabyte-size data sets without the need for sampling. Snowflake’s architecture dedicates compute clusters for each workload and team, ensuring there is no resource contention among data engineering, business intelligence, and data science workloads. Snowflake allows teams to extract and transform data into rich features with the same reliability and performance of ANSI SQL and the efficiency of functional programming and DataFrame constructs supported in Java and Python. This includes effortless and secure access to the rich ecosystem of open-source libraries used in feature extraction available through Snowflake’s integration with Anaconda.

The Snowflake Data Cloud and broader partner ecosystem can enhance the advantages of AutoML by pushing down the process of feature engineering into the Snowflake Data Cloud, boosting AutoML speeds. Snowflake also enables manual feature engineering with Python, Apache Spark, and ODBC/JDBC connectors. 

Learn more about feature engineering.

See Snowflake’s machine learning capabilities for yourself. To give it a test drive, sign up for a free trial.