The preparation procedures that turn raw data into features that may be utilized in machine learning algorithms, such as predictive models, are referred to as the feature engineering pipeline. Predictive models have an outcome variable and predictor variables, and throughout the feature engineering process, the most effective predictor variables are created and selected for the predictive model. Since 2016, some machine learning software has included automated feature engineering. In machine learning, there are four primary processes to feature engineering: feature creation, transformations, feature extraction, and feature selection.
The construction, modification, extraction, and selection of features, also known as variables, that are most conducive to constructing an accurate ML algorithm are all part of feature engineering. These procedures entail the following:
Feature Development
Identifying all relevant predictor variables to include in the model is the first stage in feature engineering. Identifying these characteristics is more of a theoretical activity that may be accomplished by reviewing relevant literature, speaking with experts in the field, and brainstorming.
When it comes to predictive modeling, one of the most common mistakes individuals make is focusing on data that is already accessible. Instead, they should think about what data is needed. This error frequently results in two issues:
- In the end, important predictor variables are left out of the model. For example, information on the type of property is critical in a model that predicts property values. If this information isn’t readily available, it must be obtained before attempting to develop a predictive model.
- Variables that should be formed from existing data aren’t. The Body Mass Index, for example, is an excellent predictor of many health outcomes (BMI). To find a person’s BMI, divide their weight by the square of their height. To design a strong predictive model of health outcomes, you must first understand why you need to include this variable as a feature in your model. If you only include height and weight in the model, the results will almost certainly be poorer than if you include BMI, height, and weight as predictors, as well as other relevant factors.
Transformation
It’s the process of altering a predictor variable in some way to improve its predictive model performance. When it comes to changing models, there are several factors to consider, including:
- Machine learning and statistical models’ versatility in dealing with many forms of data. Some strategies, for example, demand that the input data be in numeric format, whilst others can handle categories, text, or date data.
- Interpretation is simple. It is simpler to comprehend a prediction model in which all predictors are on the same scale.
- Accurate prediction. Some variables can be transformed to increase prediction accuracy.
- Error in computation. Many algorithms are built in such a way that “big” numbers lead them to provide incorrect results, even though “large” may not always be the case.
Extraction of features
Transformations are the process of producing a new variable by modifying an existing variable in some way. Feature extraction is the process of generating variables from other data.
- For instance, principal components analysis (PCA) may be used to reduce a huge number of predictor variables to a manageable quantity.
- Predictor variables are rotated orthogonally to reduce the influence of their strong correlation.
- Cluster analysis is used to convert numerous numeric variables into category variables.
- Text analytics is used to extract quantitative variables from text data, such as sentiment scores.
- To recognize shapes in photos, edge detection methods are used.
Selection of features
The choice concerning which predictor variables should be included in a model is referred to as feature selection. To a newbie, including all of the accessible features in the model may appear apparent. Then leave it up to the predictive model to figure out which ones are acceptable. Though, it is not so straightforward.
If you choose all of the potential predictor variables, the machine you’re using may crash. It’s possible that the algorithm being utilized wasn’t intended to take into account all of the available factors. If you incorporate all of a model’s potential attributes, the model may wind up detecting erroneous correlations. When you give a model a lot of data, it may frequently come up with predictions that appear to be accurate but are merely coincidences, much like individuals.
In reality, feature selection entails a mix of intuition, theories, and evaluating the efficacy of various feature combinations in a prediction model.
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