Almost all industries use artificial intelligence (AI) and machine learning (ML) today. As part of the so-called disruptive technologies, they’ve upended current technologies and have affected people in the way they work, do business, and spend their leisure time. And, with the pace these techs are advancing, they’ll continue to be at the forefront of technological progress in the next few years.
As an AI subset, machine learning gives computer systems the ability to learn from experience without being explicitly programmed. What ML needs is data—lots of it. It processes and consumes voluminous data to understand patterns, and whatever it learns is integrated into something called a predictive model. Predictive models are composed of algorithms that portray a real-world scenario using mathematics.
So, where does feature engineering fit into all these?
An Overview Of Feature Engineering
Machine learning doesn’t need instruction to teach a computer to do tasks extrapolated from a data set. But, you have to ‘feed’ its algorithm enough data to prepare it. Since this data is initially disorganized and unprocessed (called ‘raw’ data), data scientists have to extract features from these data before they’re fed to the algorithm.
This method is known as feature engineering. The steps typically used when using machine learning are data collection, cleansing, feature engineering, model definition, and training and predicting the result. Feature engineering is the trickiest component of the whole process, as well as the most crucial. It can be the difference between a bad model and a good model. A more detailed and technical treatment of this process is found here: click for source.
Essentially, feature engineering creates functional features from the raw data, which will be fed into the machine learning model. It converts messy, raw data into a more suitable form for whatever the specific objective is. Feature engineering plays a significant role in this stage, which is also known as data preprocessing.
- So, What’s A ‘Feature’?
‘Feature’ means a characteristic of data pertinent to the target problem you’re aiming to solve together with the predictive model. Different problems, however, need different features, as it wouldn’t be useful to extract the same features from the same dataset to solve another problem.
Each problem needs its own distinct and separate features from a dataset to be useful. Moreover, different algorithms also need different features for the model to work optimally. The quality of the dataset’s features will determine the quality of the predictive model’s output.
Feature engineering in machine learning is used in many applications. For example, suppose you’re writing an algorithm for filtering out spam emails and classifying legitimate emails. In this case, the features you might include are certain topics, incidence of URLs and their structures, number of misspellings, number of exclamation points (‘Congratulations!!!!!’), and others.
Feature Engineering Steps In Machine Learning
Feature engineering in machine learning includes the following major steps, determined to be the most effective in making a reliable machine learning algorithm. These include:
- Feature Creation
This step identifies the most useful variables for the predictive model. It requires a human hand, as this process is fairly subjective and requires a degree of creativity. Existing features are also added to make new features with better predictive attributes using mathematical operations, including ratio.
This involves controlling and adjusting the predictor variables to make model performance more accurate and reliable. It ensures the machine learning model has the flexibility to process different data sets.
It’s also essential that the variables and data are on the same scale, which is instrumental in making the model simple to understand. Moreover, the model’s accuracy is also improved, and because all features are ensured to remain within the model’s acceptable range, computational errors are prevented.
- Feature Extraction
Feature extraction includes creating new variables automatically by obtaining them from raw data. This step reduces the size of the dataset, which helps the machine learning model process them manageably. Other processes involved in feature extraction are edge detection algorithm, cluster analysis, principal components analysis, and text analytics.
- Feature Selection
Feature selection is where the amount of input variables for the predictive model is trimmed down. The algorithms analyze and rank the variables to ascertain the extraneous and unnecessary features that are, then, removed. Useful features are retained and prioritized, making the whole process more efficient in terms of model performance and computational cost.
- Exploratory Data Analysis (EDA)
EDA is a highly useful tool that examines and explores the data’s properties, making them easier to understand. This technique is frequently used when the target is to formulate new hypotheses or locate patterns occurring in the data. EDA is beneficial when used on a huge amount of quantitative or qualitative datasets that are yet to be analyzed.
A benchmark model is used to measure your model results—it’s a transparent, reliable, interpretable, and most user-friendly model with which to measure other model’s results. Remember, when making models, it’s always a good idea to outperform an established benchmark. It serves as a baseline standard to compare the accuracy of all variables, which can decrease errors and improve the predictive ability of the model.
Feature engineering is tremendously helpful for data scientists working with big data. It determines the success or failure of a machine learning model, and can turn raw, unprocessed data into useful variables, without which a predictive model will be pretty much useless.
The steps described here can be a valuable guide to how feature engineering works and how it affects the outcome of a machine learning model.