Feature scaling stands for transforming variable values into a certain standard range. Feature scaling can quite important for certain machine learning algorithms, such as gradient descent, support vector machine. This post is about introducing several feature scaling techniques.
Why Scale Features?
Machine Learning algorithms don't perform well when the input numerical attributes have different scales significantly (Ge), i.e. some features may range from 0 to 10 while another range from 1,000 to 10,000. This may cause some algorithms to assign these two variables with different importance.
For example, in the k-nearest neighbor algorithm, the classifier mainly calculates the Euclidean distance between two points. If a feature has a larger range value than other features, the distance will be dominated by this eigenvalue. Therefore each feature should be normalized, such as processing the range of values between 0 and 1.
In addition, feature scaling would make the algorithms (gradient descent) to converge faster.
Decision tree like algorithms are usually not sensitive to feature scales.
Methods of Feature Scaling
1. Rescaling/Min-Max Scaling
In this approach, the data is scaled to a fixed range - usually [0,1] or [-1,1]. The cost of having this bounded range is that we will end up with smaller standard deviations, which can suppress the effect of outliers.
from sklearn.preprocessing import MinMaxScaler
- enhances the stability of attributes with small variance
- keeps the 0s in a sparse matrix
Feature standardization makes the values of each feature in the data have zero-mean (when subtracting the mean in the numerator) and unit-variance (1).
This method is widely used in machine learning algorithms, e.g. SVM, logistic regression and neural networks.
from sklearn.preprocessing import StandardScaler
3. Mean normalisation
4. Scaling to unit length
Devided by the Euclidean length of the vector, two norm.