What are feature scaling and normalization? Both feature and data scaling are important processes in machine learning. Machine learning algorithms use features to compute distance between two data points, and the scales of these features are critical to their convergence. For this reason, feature scaling is necessary for most machine learning algorithms. In general, the following algorithms require feature scaling:
Feature scaling involves transforming a set of features into a single variable. It is also known as data normalization, and is usually performed during the preprocessing stage. It is important because independent variables with wide ranges may cause the learning process to be unstable or cause large loss during training and testing. Data normalization techniques include standardization and normalization. The ranges of all features should be normalized before they are used.
Feature scaling is required for models that compute distance between two points, such as logistic regression. Other types of machine learning algorithms need feature scaling, such as Linear Regression and K-Means clustering. These algorithms use the gradient descent method. The latter requires feature scaling to avoid overfitting. A few other types of algorithm do not require feature scaling. The only exceptions are the algorithms that use the distance or location of two points.
The range of feature values also affects the movement of the function. The greater the distance between two points, the more likely they will overlap, and vice versa. Scaling the data helps the function work better. In addition, feature scaling improves the convergence speed of an algorithm. It also decreases the time needed to compute support vectors. The results of an SVM are affected by the distance between two points.
Feature scaling improves the accuracy of a model by bringing values into the same magnitude. For example, if a car weighs three kilograms and travels fifty thousand kilometers, the machine learning algorithm may ignore the difference in scale and make a wrong prediction. This is where feature scaling comes into play. By scaling the range of all features, a machine learning algorithm improves its accuracy and predictability.
The ultimate necessity of data collection is data collection. Big organizations record numerous properties and attributes to prevent losing critical information. Every attribute also has a valid range. A motorbike’s speed can vary from 0 to 200 KM/h, while a car’s can go from zero to 400 KM/h. Then there are the data points that represent the differences between two data points. When these differences are bigger, the uncertainty of the result increases.