Why Feature Scaling is Crucial for Machine Learning Models

Machine learning models have become increasingly sophisticated in recent years, and their applications are now a fundamental part of many industries. However, successful implementation of machine learning algorithms is dependent on several factors one of which is feature scaling. In this article, we will explore the importance of feature scaling and how it impacts machine learning models.

What is Feature Scaling?

Feature scaling is the process of standardizing the range of features in a dataset. In other words, it is the technique used to bring all features to the same level of magnitude, so that one feature does not dominate the others. It involves scaling all feature values to be within a specific range or to follow a specific distribution. This process is especially important when using distance-based algorithms, as the distance between different features can significantly affect the accuracy of the model.

Why is Feature Scaling Important for Machine Learning?

Machine learning algorithms rely on mathematical equations and models, and feature scaling helps these models to converge more quickly and accurately. In many cases, if feature scaling is not performed, the model may not find the optimal solution or may take much longer to find it. For instance, consider a dataset with two features: age and income. If they are not scaled, the distance between the two features will be dominated by the income feature as it will have a much larger range of values; the age feature will be overlooked, and the model will end up relying solely on income to make predictions. Furthermore, scaling is also critical when handling features with different measurement units or scales, such as height in centimeters and weight in kilograms.

Types of Feature Scaling

There are different ways to perform feature scaling, namely:

• Standardization: Involves scaling the features to have zero means and unit variances. This is typically the most popular technique as it maintains the shape of the data distribution.
• Normalization: Involves scaling the features to have values ranging between 0 and 1.
• MinMax Scaling: Involves scaling the features to have values between a specified minimum and maximum range.

Real-World Example: Gradient Descent Algorithm

Gradient descent is a popular algorithm used to minimize the cost function of a machine learning model. In this algorithm, feature scaling plays a significant role in finding the minimum point efficiently. Suppose the cost function has two variables, one ranging from $0$ to $2000$, and another ranging from $0$ to $5$. If we do not scale these features, the algorithm will take a longer time to converge, and as a result, the model will take much longer to train. However, by performing feature scaling, the algorithm can converge much faster, saving both time and cost.

Conclusion

It is evident that feature scaling is a crucial aspect of building successful machine learning models. It helps to eliminate the dominance of one feature over the others, allowing the model to understand the data better and make more accurate predictions. Different techniques of feature scaling exist, each suited for specific instances, and it is essential to choose the appropriate method for each scenario.