What is Standardization || Explanation with Implementation Example|| Data Science @datatechinfo
Standardization is a data preprocessing technique used in machine learning to rescale the features of a dataset to have zero mean and unit variance. The goal is to transform the data in a way that each feature contributes equally to the model training process, preventing certain features from dominating due to differences in their original scales.
The standardization process involves subtracting the mean of each feature from its values and then dividing by the standard deviation. This results in a distribution with a mean of 0 and a standard deviation of 1.
Benefits of Standardization:
Helps algorithms converge faster during training.
Makes models less sensitive to the scale of input features.
Facilitates better comparison and interpretation of feature importance.
#Standardization
#DataPreprocessing
#FeatureScaling
#MachineLearning
#DataNormalization
#ZeroMeanUnitVariance
#DataTransformation
#ScalingTechniques
#StatisticalMethods
#ModelTraining