Data Preprocessing Questions Medium
Feature scaling is a crucial step in data preprocessing that involves transforming the numerical features of a dataset to a common scale. It is necessary because many machine learning algorithms are sensitive to the scale of the input features. When features have different scales, it can lead to biased or incorrect predictions.
There are two main reasons why feature scaling is necessary in data preprocessing. Firstly, it helps to avoid the dominance of certain features over others. When features have different scales, those with larger values can dominate the learning process, leading to inaccurate results. By scaling the features, we ensure that each feature contributes proportionally to the learning process.
Secondly, feature scaling helps to improve the convergence speed and performance of many machine learning algorithms. Algorithms like gradient descent, which are commonly used for optimization, converge faster when the features are on a similar scale. This is because large differences in feature scales can cause the optimization process to take longer or even fail to converge.
There are various techniques for feature scaling, including normalization and standardization. Normalization scales the features to a range between 0 and 1, while standardization transforms the features to have zero mean and unit variance. The choice of technique depends on the specific requirements of the dataset and the machine learning algorithm being used.
In conclusion, feature scaling is necessary in data preprocessing to ensure that all features contribute equally to the learning process and to improve the convergence speed and performance of machine learning algorithms.