Feature Engineering in ML

Hey reader👋
We know that we train machine learning model on a dataset and generate prediction on any unseen data based on training. The data which we are using here must be structured and well defined so that our algorithm can work efficiently. To make our data more meaningful and useful for our algorithm we perform Feature Engineering on our dataset. Feature Engineering is one of the most important steps in Machine Learning.
In this blog we are going to know about Feature Engineering and its importance. So let’s get started🔥

Feature Engineering

Feature Engineering is the process of using domain knowledge to extract features from raw data. These features can be used to improve the performance of Machine Learning algorithm.

So here you can see that we are working on a dataset, as a very first step we are processing data then we are extracting the important features using feature engineering then we are scaling the features i.e. transforming features in same unit. Once feature engineering is performed on dataset we are applying algorithm and then evaluating metrics. For better performance of model we are again performing feature engineering on the dataset till we get a good model.

Why Feature Engineering?

1. Improves Model Performance: Well-crafted features can significantly enhance the predictive power of our models. The better the features, the more likely the model will capture the underlying patterns in the data.

2. Reduces Complexity: By creating meaningful features, we can simplify the model’s task, which often leads to better performance and reduced computational cost.

3. Enhances Interpretability: Good features can make our model more interpretable, allowing us to understand and explain how the model makes its predictions.

Key Techniques in Feature Engineering

The key techniques of Feature Engineering are -:

1. Feature Transformation -: We can transform features so that our model can perform effectively on it and give better results. This generally involves -:

• Missing Value Imputation -: Techniques include imputation (filling missing values with mean, median, or mode), or using algorithms that can handle missing data directly.

• Handling Categorical Data -: Converting categorical variables into numerical ones using methods like one-hot encoding or label encoding.

• Outlier Detection -: Identifying and removing outliers can help in creating robust models.

• Feature Scaling -: Scaling features to a standard range or distribution can improve model performance, especially for distance-based algorithms.

1. Feature Construction -: Sometimes to make our data more meaningful we add some extra information in our data based on existing information. This process is called Feature Construction. This can be done in following ways -:

• Polynomial Features: Creating interaction terms or polynomial terms of existing features to capture non-linear relationships.

• Domain-Specific Features: Using domain knowledge to create features that capture essential characteristics of the data. For example, in a financial dataset, creating features like debt-to-income ratio or credit utilization.

• Datetime Features: Extracting information such as day, month, year, or even whether a date falls on a weekend or holiday can provide valuable insights.

1. Feature Selection -: Feature Selection is the process of selecting a subset of relevant features from the dataset to be used in a machine learning model. The different techniques we use for feature selection are -:

• Filter Method: Based on the statistical measure of the relationship between the feature and the target variable. Features with a high correlation are selected.

• Wrapper Method: Based on the evaluation of the feature subset using a specific machine learning algorithm. The feature subset that results in the best performance is selected.

• Embedded Method: Based on the feature selection as part of the training process of the machine learning algorithm.

1. Feature Extraction -: Feature Extraction is the process of creating new features from existing ones to provide more relevant information to the machine learning model. This is important in machine learning because the scale of the features can affect the performance of the model. The various techniques used for feature extraction are -:

• Dimensionality Reduction: Reducing the number of features by transforming the data into a lower-dimensional space while retaining important information. Examples are PCA and t-SNE.

• Feature Combination: Combining two or more existing features to create a new one. For example, the interaction between two features.

• Feature Aggregation: Aggregating features to create a new one. For example, calculating the mean, sum, or count of a set of features.

• Feature Transformation: Transforming existing features into a new representation. For example, log transformation of a feature with a skewed distribution.

So this was an introduction to feature engineering. In the upcoming blogs we are going to study about each technique separately. Till then stay connected and don’t forget to follow me.
Thankyou ❤