Feature Selection for Accurate Machine Learning Models

The human brain is continuously filtering through incoming data, determining what information is important and what’s just noise. This helps us to focus on what matters and find actionable meaning in information quickly. Feature selection helps machine learning (ML) models do something similar: sift through data points to determine which ones are relevant to the use case and which aren’t. 

What Is Machine Learning Feature Selection?

A part of feature engineering, machine learning feature selection is the process that data scientists use to select the training data most likely to result in a model capable of producing accurate, consistent outputs. Features are the measurable properties of an object, or data points. 

For example, a data scientist seeking to create a machine learning model that can help detect email spam may include features such as characteristics of the sender’s email address, email length, content of the subject line, and grammatical construct. These features are highly relevant to spam detection efforts. Including less relevant features, such as the time the email was sent or the length of the message, introduces additional data that will slow the learning process and may result in less accurate predictions. Selecting the features with the most relevant data for a model requires both business acumen and analytical skills. Learn more about the types of features possible in this ebook.

Machine Learning Feature Selection Methods

Manually selecting the right set of features isn’t time/cost effective, given there can be hundreds to thousands of variables to peruse. Instead, data scientists use algorithms to help them identify the best subset of features to meet the goal of their machine learning project. These algorithms may use a variety of methods to speed up the feature selection process.

Filter methods

Filter methods examine features based on how they correlate to the output. Each feature is examined individually and compared to other features. Based on the characteristics in the data, the algorithm determines whether or not the feature should be filtered out or included. 

Wrapper methods

Wrapper methods take the “greedy approach” to feature selection, an algorithmic strategy that creates many models with different subsets of input features to arrive at the best overall solution. These algorithms evaluate the accuracy of every possible combination of features, automatically adding and subtracting them until it identifies the feature subset most likely to produce the most accurate output. For example, recursive feature elimination (RFE) selects features by grouping them into sets and considering the importance of each feature within the set, pruning out features until the process is complete. Then the procedure is recursively repeated on the newly pruned set, resulting in smaller and smaller sets of features.

Embedded methods

Embedded methods combine certain characteristics and benefits of the filter and wrapper methods. With embedded methods, feature selection happens in tandem with the model fitting. This approach makes it possible to consider the interaction between the model and the features, allowing the features that contributed the most to the success of each iteration to be identified and extracted.

Hybrid methods

As the name implies, hybrid methods blend aspects of each of the three filter selection methods above to arrive at the ideal feature subset. 

How Does Feature Selection Create Better Machine Learning Models?

Machine learning feature selection is an important part of building high-quality machine learning applications. Here are five ways feature selection optimizes the value of machine learning models.

Reduces noise

Machine learning models depend on relevant training data to learn. Including machine learning features that aren’t relevant creates noisy data that makes it more difficult for models to accurately generalize. 

Speeds up training and inference times

Each machine learning feature added to a model increases its overall complexity, slowing down training times while increasing the computing resources the model consumes. Machine learning models with an optimal number of features can score incoming data more quickly and efficiently. 

Less prone to data-dependent errors

Fewer features shield the model from exposure to data-dependent errors coming from the data sources being used. The less data the model needs to pull in, the less chance there is for that data to be flawed or unavailable altogether.

Simpler to understand

The more features a machine learning model has, the more difficult it is to understand how each variable is influencing the outcome. Tracking how each feature affects the model’s performance simplifies the model training and fitting process. 

Improved accuracy

Machine learning feature selection has a direct impact on the performance of the model once it’s deployed in real-life settings. Models that are using the right features create higher-quality, more-precise outputs, not only during development but also when running in production. 

Common Barriers to Adopting ML and Getting Models to Production 

Machine learning has applications in a diverse range of industries including healthcare, manufacturing, finance, and marketing. But implementing machine learning and getting models to production requires overcoming a unique set of challenges.

Data security

Data security is one of the main roadblocks when it comes to machine learning. According to Anaconda’s 2022 State Data Science report, 28.45% of respondents said that securing data connectivity was the top challenge they faced with getting machine learning models to production. And 33.88% cited meeting IT/InfoSec standards when moving models to

a production environment. To meet security needs, it’s crucial to partner with vendors that bake security into their platforms and applications and have achieved relevant government and industry data security compliance.

Overfitting/underfitting data models

Overfitting describes when a machine learning model is performing well with the training data but isn’t able to generalize to other data. This can be caused by a number of factors including using too many features or failing to remove troublesome outliers. Underfitting involves poor performance on the training data and poor performance with generalizing to other data. Issues with underfitting can be resolved by improved machine learning feature selection and removing noisy data from training sets. 

Access to relevant data sources 

Highly relevant data is the fuel machine learning algorithms need to perform at their peak. In many organizations, this data is siloed in a patchwork of data systems or held by vendors and data partners. To implement machine learning in production, all relevant data used for training must be available in a single platform as part of automated pipelines that feed features to models generating predictions.

Inadequate volume of data

Models require large quantities of training data to meet their intended purpose. When data is scarce, training models may be underfitted, resulting in models that don’t perform well with production data due to the limited set of knowledge learned in the underlying data set used for training.

Insufficient infrastructure 

Machine learning requires a significant amount of computing power to execute feature preparation pipelines as well as train and deploy models. Depending on the complexity of the program, data science teams with compute resource limitations can create new analytic silos as they embark on building their own data infrastructure or will have a slow pace of innovation as they encounter resource contention with other teams and applications.

Snowflake for Machine Learning

The Snowflake Data Cloud is ideal for deploying a full range of machine learning applications. With Snowflake, organizations can reduce time looking for and requesting access to data, with a single point of access to a global network of trusted data. Data scientists have native support for structured, semi-structured (JSON, Avro, ORC, Parquet, or XML), and unstructured data—without complex pipelines. 

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 with Snowpark. And through Snowpark for Python, teams can effortlessly and securely access a rich ecosystem of open-source libraries used in feature extraction available through Snowflake’s integration with Anaconda.

Snowflake also makes it easy to augment model performance with shared data sets from your business ecosystem and third-party data from Snowflake Marketplace. And with Snowflake’s elastic and performant multi-cluster compute architecture, you can easily scale processing to any amount of data or users. Effortlessly make model results available in Snowflake for teams and applications to easily act on machine learning–driven insights.

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