How to Engineer New Features for Better Model Performance

Overview:

Feature engineering is a power full tool for improving the performance of machine learning models. Basically, it involves inn creating a new input feature or transforming existing ones to help models learn more effectively from the data. In this blog we are going to see how the engineering features are going to be perform and how the model produces the accurate values after the feature engineering is done.

What is feature Engineering?

Feature Engineerinng is the process of creating a new features based on the domain knowledge and the data insights for the machine learning models to produce more accurate and predictive.

Why is it important?

It is more Important because out model can be producing more accurate results based on the engineering features. Some of the key points are:

• Reveal Hidden patterns
• Simplify the relationships
• Reduce noise in the dataset
• Improve generalization

Techniques of Feature Engineering:

1. Transformation: This technique is helpful for changing the shape or distributions of a feature
2. Encoding Categorical: Converting the Categorical Data into the Numerical Data.
3. Binning: This is also known as Discretization. This technique is used to turn the continuous variables into the categories.

Once the Engineering Features are created then perform on the model so that the mode can be produces the accurate results

Let’s a small example for the engineering feature:

sample medical dataset (before feature engineering):

patient_id                Age         Weight       Blood Pressure      diagnosis_time                  diagnosis

p1                                   45              80                        140/90                  2024-02-01 (09:50:06)           hypertension

p2                                   60              90                        160/100                2024-02-02 (10:56:00)          hypertension

p2                                   70               95                         120/80                  2024-02-03 (09:20:36)           healthy

 

Final output after the Feature Engineering:

patient_id    Age       Weight     BMI       Hours     Systolic         Diastolic       High_risk

p1                       45              80             27.62        2                   140                  90                   Little

p2                      60              90              36.29        1                     160                 100                 High

p3                      70              95               20.06       3                     120                  80                 Normal

 

Conclusion:

Based on the above blog I have concluded that feature engineering is more important for the machine learning models to produce the more accurate and predictable outputs.