Lesson 5

Harnessing Feature Combinations for Enhanced Machine Learning Models

Overview

Welcome to a deep dive into an intriguing aspect of data science - feature combinations! This lesson will bring you up to speed on the methods and principles behind creating, understanding, using, and validating feature combinations. By the end of this session, you'll be familiar with how they can enhance your Machine Learning model's performance.

What are feature combinations, you may ask? Imagine you're tasked with predicting the price of a house. You might have features like Number of Rooms and Square Footage. While they are useful features themselves, creating new ones by combining or transforming the existing ones could provide a more nuanced picture of your data. For example, creating a new feature, Area per Room, might capture more valuable information. Let's dive in deeper!

Conceptual Understanding of Feature Combinations

Before we start coding, it's important to understand the core principles of feature combinations. These involve aggregating two or more existing features to create a new one, usually through operations such as addition, subtraction, multiplication, or division. They enhance our data by generating new attributes or 'features' that extend our perspective on the data, potentially uncovering hidden patterns that improve our model's predictive accuracy.

However, you should be cautious about creating feature combinations without carefully considering your data and the problem at hand. Always ground your rationale in domain knowledge and the context of your data. Now that we've clarified the theory, let's put our knowledge into practice with some Python code!

Generating Feature Combinations

Here is an example of feature combination that can be applicable to the UCI Abalone Dataset! Note that we separate out the numeric features so we can compute correlation.

Python
1# Isolate features and targets 2X = abalone.data.features 3Y = abalone.data.targets 4 5# Convert targets to a DataFrame and merge with the features DataFrame 6targets_df = pd.DataFrame(Y, columns=['Rings']) 7abalone_data = pd.concat([X, targets_df], axis=1) 8 9# Remove non-numeric columns and create new feature combinations 10abalone_numeric = abalone_data.select_dtypes(include=[np.number]) 11abalone_numeric["Length_Diameter_Ratio"] = abalone_numeric["Length"] / abalone_numeric["Diameter"] 12abalone_numeric["Length_Height_Ratio"] = abalone_numeric["Length"] / abalone_numeric["Height"]

This code demonstrates a simple way of generating a new feature by dividing two existing ones. This ratio could reveal hidden patterns not visible when considering the features independently. It's all about perspective!

Validating Feature Combinations

Creating many feature combinations can be fun, but not all of them are necessarily useful. In fact, some might introduce unnecessary complexity or even mislead our models. That's where feature selection techniques come into play, like the correlation matrix. These techniques help us assess the importance of our newly crafted features. Let's see how we can do this using the pandas library:

Python
1# Compute the correlation of all numeric features with the 'Rings' target 2correlation = abalone_numeric.corr()['Rings'] 3 4# Print the correlation of the new features with 'Rings' 5print(correlation[['Length_Diameter_Ratio', 'Length_Height_Ratio']])

output:

1Length_Diameter_Ratio -0.345301 2Length_Height_Ratio -0.226854 3Name: Rings, dtype: float64

The above output indicates how these two new features are inversely correlated to the target variable (the count of rings). This can help us understand that we are introducing noise to the features instead of creating helpful features.

Improving Feature Correlation

Having seen previously that certain ratios are negatively correlated with Rings, let's explore a different kind of feature combination aimed at obtaining a more positive correlation, which often implies a more direct relationship with the target variable.

When crafting feature combinations, it's insightful to consider interactions between measurements that, when combined, might reflect an attribute that develops in proportion to the age of abalones—such as their overall size or the 'footprint' space they occupy.

Here's an example that aims to capture the overall physical scale of the abalone by multiplying Length and Diameter:

Python
1# Create a new feature representing the product of 'Length' and 'Diameter' 2abalone_numeric["Length_x_Diameter"] = abalone_numeric["Length"] * abalone_numeric["Diameter"]

By multiplying these two dimensions, we create a feature that approximates the surface area, potentially giving us a more correlated variable to the age than either dimension alone. Now, let's examine how our new feature fares in terms of correlation:

Python
1# Calculate and print the correlation coefficient of the new feature with 'Rings' 2print(correlation[['Length_x_Diameter']])

output:

1Length_x_Diameter 0.549009

Seeing the positive correlation from this combination, we can infer that as abalones grow in physical size, their ring count—indicative of age—also tends to increase, and this new feature captures that relationship effectively. The higher the positive correlation coefficient, the stronger this newly created feature is associated with the abalone's age. This guides us to include Length_x_Diameter in our predictive models, given its potential to improve accuracy.

Executable Code

Here is a complete example that can be run to show you the correlation of the three different features.

Python
1# Import necessary libraries 2from ucimlrepo import fetch_ucirepo 3import numpy as np 4import pandas as pd 5 6# Collect the UCI Abalone dataset 7abalone = fetch_ucirepo(id=1) 8 9# Isolate features and targets 10X = abalone.data.features 11Y = abalone.data.targets 12 13# Convert targets to a DataFrame and merge with the features DataFrame 14targets_df = pd.DataFrame(Y, columns=['Rings']) 15abalone_data = pd.concat([X, targets_df], axis=1) 16 17# Remove non-numeric columns and create new feature combinations 18abalone_numeric = abalone_data.select_dtypes(include=[np.number]) 19abalone_numeric["Length_Diameter_Ratio"] = abalone_numeric["Length"] / abalone_numeric["Diameter"] 20abalone_numeric["Length_Height_Ratio"] = abalone_numeric["Length"] / abalone_numeric["Height"] 21abalone_numeric["Length_x_Diameter"] = abalone_numeric["Length"] * abalone_numeric["Diameter"] 22 23# Compute the correlation of all numeric features with the 'Rings' target 24correlation = abalone_numeric.corr()['Rings'] 25 26# Print the correlation of the new features with 'Rings' 27print(correlation[['Length_Diameter_Ratio', 'Length_Height_Ratio', 'Length_x_Diameter']])
Lesson Summary & Practice

That wraps up our lesson on feature combinations! You've now gained a solid understanding of what feature combinations are, how to generate valuable ones in Python using pandas and how to inspect their importance using correlation.

As with any rich subject, mastering feature combinations requires practice. Hence, we've prepared a series of hands-on exercises to cement your knowledge and elevate your data manipulation skills. Are you ready to put your skills to the test with real-world datasets and uncover some hidden patterns? Let's dive right in!

Enjoy this lesson? Now it's time to practice with Cosmo!

Practice is how you turn knowledge into actual skills.