What Is Feature Engineering?
Feature Engineering is the process of selecting, extracting, and changing raw data into features that a machine learning algorithm can use to make predictions or classifications. The quality and relevance of the features can have a significant impact on the performance of the model.
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It helps to gain a deeper understanding of the data by identifying patterns, relationships, and trends that may not be immediately apparent. This understanding can lead to new insights and opportunities for further analysis. It also helps to improve the scalability of the model by reducing the dimensionality of the input data and making it easier to process and analyze.
Table of contents
- What Is Feature Engineering?
- Feature Engineering Explained
- Techniques
- Examples
- Importance
- Feature Engineering And Data Preprocessing
- Feature Engineering vs. Feature Selection vs. Feature Extraction
- Frequently Asked Questions (FAQs)
- Recommended Articles
Key Takeaways
- Feature engineering is the process of converting raw data into a set of features that can be used to teach machine learning models to form valuable insights.
- Feature engineering aims to create informative, relevant, and useful features that capture the most important information in the data that can be used to accurately predict the target variable in the machine learning model.
- It involves a range of techniques, such as data preprocessing, data transformation, feature selection, and feature extraction.
Feature Engineering Explained
Feature engineering is s a crucial step in the machine learning pipeline as it converts raw data into features that help to make predictions or classifications. It has a significant impact on the performance of the resulting model. The goal of feature engineering is to create informative, uncorrelated features and have a strong relationship with the target variable.
Steps
There are various steps involved in feature engineering that include:
- Feature Selection: This step involves selecting the most relevant features from the raw data. The goal is to choose features that are informative, uncorrelated, and have a strong relationship with the target variable.
- Feature Extraction: This step involves creating new features from the raw data. The goal is to transform the data into a format that is more suitable for the machine learning algorithm.
- Feature Transformation: This step involves transforming the features into a format that is suitable for the machine learning algorithm. Common techniques for feature transformation include normalization, scaling, or log transformations.
- Feature Augmentation: This step involves adding new features to the dataset that can provide additional information to the machine learning algorithm. Feature augmentation can involve adding new features derived from external sources, such as weather data or demographic information.
Techniques
The following techniques used in feature engineering are as follows –
- Feature Encoding: This step involves encoding categorical data into a format that can be used by the machine learning algorithm. Common techniques for feature encoding include one-hot encoding, label encoding, and binary encoding.
- Feature Scaling: This step involves scaling the features so that they are on the same scale. This can be important if the features have different units or scales, as it can make it easier for the machine learning algorithm to compare the features.
- One-Hot Encoding: This is a technique used to convert categorical variables into numerical values by creating a binary column for each category. For example, if there is a categorical feature like color with categories red, blue, and green, then one-hot encoding will create three binary columns representing each category.
- Discretization: Discretization is a technique used to convert continuous variables into discrete values to simplify the model. For example, age can be discretized into age groups like 0-10, 11-20, 21-30, etc.
- Binning: Binning is a technique used to group continuous variables into bins based on specific intervals. For example, income can be binned into income ranges like low-income, middle-income, and high-income.
- Imputation: Imputation is a technique used to fill in missing values in a dataset. Various imputation techniques are available like mean imputation, median imputation, and mode imputation.
Examples
Let us look at the following examples to understand the concept better:
Example #1
Let’s say John is working on a project to predict the likelihood of a student passing a test based on their performance in previous tests and other factors. One potential feature John could engineer is the “average score improvement” for each student between two consecutive tests.
To create this feature, John would first collect data on the student’s test scores, which might look something like this:
| Student ID | Test 1 Score | Test 2 Score | Test 3 Score |
|---|---|---|---|
| 1 | 70 | 80 | 85 |
| 2 | 60 | 65 | 70 |
| 3 | 80 | 90 | 95 |
John calculates the average score improvement between two consecutive tests by subtracting the previous test score from the current test score and dividing it by the number of tests taken. For example, for student 1, the average score improvement between test 1 and test 2 would be:
(80 – 70) / 1 = 10
And the average score improvement between test 2 and test 3 would be:
(85 – 80) / 1 = 5
John could then use these average score improvement values as features in your model to predict the likelihood of a student passing the next test.
Example #2
Let’s say Erica is working on a project to predict the likelihood of a customer churning (canceling their subscription) based on their usage of a mobile app. One potential feature she could engineer is the “frequency of app usage” for each customer.
To create this feature, Erica would collect data on the customer’s app usage, such as the number of times they opened the app each day or week. However, simply using the raw count of app opens as a feature may not provide enough information to accurately predict churn. A customer who opens the app ten times per week may not necessarily be more likely to churn than a customer who opens the app five times per week if the former spends more time and engages more with the app than the latter.
To address this issue, Erica could engineer a new feature by calculating the “average session duration” for each customer. This would involve recording the start and end times for each app session and calculating the average time spent in the app during each session for each customer.
By combining the “frequency of app usage” with the “average session duration” features, Erica can get a complete picture of each customer’s engagement with the app. This can improve the accuracy of the churn prediction model by capturing the quality of the customer’s interaction with the app, in addition to the quantity of usage.
Importance
Feature engineering is an important step in building machine learning models because the quality of the features used directly impacts the accuracy and performance of the model. Here are some reasons why feature engineering is crucial:
- Better representation of the data: Feature engineering allows representation of the data in a more effective way that captures the underlying patterns and relationships. By selecting or creating relevant features, they can reduce noise, increase signal, and improve the performance of your model.
- Addressing missing or noisy data: Feature engineering techniques such as imputation and outlier detection can help address missing or noisy data. This can improve the quality of the data and make it more suitable for analysis.
- Improved interpretability: Carefully selected features can make the model more interpretable by providing insights into which features are driving the predictions. This can help domain experts understand the model better and make more informed decisions.
- More efficient computation: Feature engineering can also reduce the dimensionality of the data by selecting the most relevant features. This can reduce the computational resources required to build and deploy the model, making it more efficient.
Feature Engineering And Data Preprocessing
The differences between feature engineering and data preprocessing is as follows –
| Basis | Feature Engineering | Data preprocessing |
|---|---|---|
| Meaning | Feature engineering, on the other hand, involves creating new features from the existing data to improve the performance of machine learning models. | Data preprocessing refers to the process of cleaning and transforming raw data into a format that is suitable for analysis |
| Focused | Feature engineering is focused on improving the representation of the data, and may involve complex domain-specific knowledge or creativity. | Data preprocessing is focused on preparing the data for analysis and does not involve creating new features. |
Feature Engineering vs Feature Selection vs Feature Extraction
The differences between feature engineering vs. feature selection vs. feature extraction is as follows –
| Basis | Feature Engineering | Feature Selection | Feature Extraction |
|---|---|---|---|
| Meaning | Feature engineering involves creating new features from the existing data to improve the performance of machine learning models. | Feature selection, on the other hand, involves selecting a subset of the available features that are most relevant for a given predictive modeling problem. | Feature extraction involves transforming the original data into a new feature space using mathematical techniques such as Principal Component Analysis (PCA) or Linear Discriminant Analysis (LDA). |
| Purpose | This may involve tasks such as transforming variables, creating interaction terms, or encoding variables in a way that captures relevant information. | The goal of feature selection is to reduce the dimensionality of the data, which can help to reduce overfitting, improve model performance, and speed up training | The goal of feature extraction is to identify and extract the most important and relevant information from the original data while reducing the dimensionality of the data. |
Frequently Asked Questions (FAQs)
Feature engineering in Python involves creating new features from existing data to improve the performance of machine-learning models. This can involve tasks such as:
Feature selection: Selecting a subset of the available features that are most relevant for a given predictive modeling problem.
Feature extraction: Transforming the original data into a new feature space using mathematical techniques such as Principal Component Analysis (PCA) or Linear Discriminant Analysis (LDA).
Data transformation: Transforming variables using mathematical functions like log transformations or polynomial expansions.
Yes, feature selection is a part of feature engineering. Feature selection involves selecting a subset of the available features that are most relevant for a given predictive modeling problem. This is a crucial step in the feature engineering process, as selecting the right set of features can help to reduce overfitting, improve model performance, and speed up training.
Yes, feature engineering is typically considered as a crucial part of the data preprocessing stage in machine learning. Feature engineering involves selecting, extracting, transforming, and creating new features from the available data to improve the performance of machine learning algorithms.
Recommended Articles
This has been a guide to what is Feature Engineering in Machine Learning. We explain its techniques, examples, comparison with data preprocessing, and importance. You can learn more about it from the following articles –
- Ridge Regression
- Boosting
- Data Lake
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