Data preprocessing is a crucial step in the machine learning pipeline, ensuring that the dataset is ready for training.
One essential aspect of data preprocessing is β¨feature scalingβ¨, which involves adjusting the range and distribution of the data.
𧡠π
One essential aspect of data preprocessing is β¨feature scalingβ¨, which involves adjusting the range and distribution of the data.
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Feature scaling techniques, such as:
πΉ normalization
πΉ standardization
ensure that each attribute contributes equally to the algorithm's performance, thereby enhancing the predictive model's accuracy and efficiency.
πΉ normalization
πΉ standardization
ensure that each attribute contributes equally to the algorithm's performance, thereby enhancing the predictive model's accuracy and efficiency.
Both normalization and standardization aim to re-scale features, but they do so in different ways and are suitable for different types of data and machine learning algorithms.
1οΈβ£ Normalization:
It is a technique used to scale the features to a similar range. The goal is to transform them to lie in a certain interval, usually [0,1], making it easier for algorithms to interpret them β equal contribution to the computation of distances or gradients.
It is a technique used to scale the features to a similar range. The goal is to transform them to lie in a certain interval, usually [0,1], making it easier for algorithms to interpret them β equal contribution to the computation of distances or gradients.
π This is why it is especially important for distance-based algorithms like KNN, or when your features have different units or vastly different scales.
2οΈβ£ Standardization:
Standardization, unlike normalization, transforms the features in a way that the resulting distribution has a mean of 0 and a standard deviation of 1. It doesn't bound values to a specific range.
Standardization, unlike normalization, transforms the features in a way that the resulting distribution has a mean of 0 and a standard deviation of 1. It doesn't bound values to a specific range.
π This is especially useful for algorithms that assume the input variables to have a Gaussian distribution.
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