Feature engineering consists of the selection, manipulation, and transformation of raw data into features used in supervised learning. The purpose and power of feature engineering and selection is to improve the performance of machine-learning algorithms. In return, model accuracy on unseen data is improved.

It also leverages the information in the training set to create new variables. Simplification and speeding up data transformations, feature engineering can enhance model accuracy by producing new features for supervised and unsupervised learning.

Illustrations of feature engineering:

1. Continuous data

This type of data can take any value from a given range. For example, the price of a product or the coordinates of some geographical feature.

The feature generation solely depends on the domain data. If we diminish the warehouse price from the shelf price, the profit can be calculated. Similarly, if the distance between two locations on a map is calculated, then the distance can be determined.

The possibilities for new features are limited only by the available features and known mathematical operations.

2. Categorical features

The second most popular type is categorical data, which refers to features that can take on values from a limited set. In most scenarios, the feature can only take a single value.

It can also happen otherwise, but in that case, the feature is usually separated into a set of features. E.g., Defining four genders: not known, male, female, and not applicable.

3. Text features

Feature engineering also involves converting text into a set of representative numerical values. All automatic mining of social media data encodes the text as numbers. It is easiest to encode data by word count.

4. Image features

Another common need for machine learning analysis is to encode images appropriately.

Importance of FE

Features engineering comprises several key components viz., such as the selection of relevant features, dealing with missing data, data encoding, and normalization. Determining a model's output is one of the most vital tasks. If the wrong hypotheses are provided, then the accuracy of the Machine Learning model will not be obtained as expected. The quality of the view will determine the success of the machine learning model.

A good feature is critical for accuracy and interoperability. By examining the essential variables and removing irrelevant variables, feature selection improves the machine learning process and increases the predictive power of the same.

Like that of the correlation matrix, feature importance allows you to comprehend the relationship between the features and the target variable. In addition, it indicates which features are redundant to the model.

Feature engineering vs. feature selection

With feature engineering, more complex models can be created than when working with raw data. It also allows for the development and interpretation of interpretable models from any amount of data. The feature selection process will help decrease the number of features to a reasonable number.

Feature engineering vs. feature extraction

Feature engineering is the conversion of raw data into features/attributes that reflect the underlying structure of the data. Feature extraction is the procedure of transformation of raw data into its desired form.

Feature engineering vs. hyperparameter tuning

Feature engineering uses data to create features that make machine learning algorithms perform based on the right set of Hypotheses. Hyperparameter tuning or optimization refers to selecting a specific set of optimal hyperparameters for a learning algorithm.

Hyperparameter optimization strives to improve model performance by altering model parameters. A feature reduction would be an illustration of feature engineering related to data.