Feature Transformation in Machine Learning || Feature Engineering

Hey reader 👋 Hope you are doing well 😊

As you know, to get accurate predictions, our model should be trained well. For better training, our data should be processed properly. To gain valuable insights from data, we perform Exploratory Data Analysis (EDA). Using EDA, we engage in Feature Engineering to transform our data as required.

In the process of Feature Engineering, we handle categorical data, missing values, outliers, feature selection, etc. Transforming numerical values is one of the critical tasks in Feature Engineering. This transformation allows us to convert all data into the same unit, making our data more efficient for model training.

In this blog, we will discuss different types of transformations and their importance. So let's get started 🔥

Feature Transformation

Feature Transformation refers to the process of converting data from one form to another. For example, transforming categorical data into numerical data, scaling numerical data, and converting data so that it follows the desired statistics of an algorithm (e.g., linear regression works well when the data is normally distributed).

The different types of Feature Transformation are -:

1. Function Transformers

2. Power Transformers

3. Feature Scaling

4. Encoding Categorical Data

5. Missing Value Imputation

6. Outlier Detection

Why is Feature Transformation Required?

Imagine trying to solve a jigsaw puzzle with pieces that don’t quite fit together. In the same way, raw, unprocessed data might not fit the requirements of your machine-learning algorithms. Feature transformation is the process of reshaping those pieces, making them compatible and coherent, and ultimately, revealing the full picture.

Machine learning algorithms often work better with features transformed to have similar scales or distributions. Feature transformation can lead to better model performance by improving the model’s ability to learn from the data.

Feature transformation can reveal hidden patterns or relationships in the data that might not be apparent in the original feature space. By creating new features or modifying existing ones, you can expose valuable information that your model can use to make more accurate predictions.

In some cases, feature transformation can help reduce the dimensionality of the data. This not only simplifies the modeling process but also helps prevent issues like the curse of dimensionality, which can lead to overfitting.

A brief about different Feature Transformation techniques

• Function Transformers -: Function transformers are the type of feature transformation technique that uses a particular function to transform the data to the normal distribution.

• Power Transformers -: Power Transformation techniques are the type of feature transformation technique where the power is applied to the data observations for transforming the data. Techniques like Box-Cox or Yeo-Johnson transformations are used to make data more normally distributed, which can be beneficial for certain algorithms.

• Feature Scaling -: Feature Scaling is a feature engineering technique that is used to transform the complete data in single scale. It either scales up the data or scales down as per requirement.

• Encoding Categorical Data -: All the machine learning algorithms are suitable for numerical data, so it is very important to convert categorical data into numerical.

• Missing Value Imputation -: Sometimes our dataset may contain missing values which can affect our model significantly. so missing values should be handled properly.

• Outlier Detection -: Outliers are datapoints that exhibit completely different behavior than rest other points in dataset, these can hinder model performance. So these should be handled properly.

So this is it for this blog in the next blog we will see how Feature Scaling is performed. Till then stay connected and don't forget to follow me.
Thankyou 💜