Data Scaling

Lecture 22:- Data Scaling

Data scaling, also known as feature scaling or data normalization, is a data preprocessing technique used to bring all numerical features in a dataset to a similar scale. The goal of data scaling is to ensure that each feature contributes equally to the analysis or modeling process, regardless of their original scale or units of measurement. Scaling is essential when features have different magnitudes or ranges because certain algorithms are sensitive to the scale of the input data.

There are various methods for data scaling, including:

Min-Max Scaling (Normalization): Min-Max scaling scales the data to a fixed range, typically between 0 and 1. It is achieved by transforming each data point x using the formula:

Standardization (Z-score Scaling): Standardization scales the data to have a mean of 0 and a standard deviation of 1. It is achieved by transforming each data point x using the formula:

Robust Scaling: Robust scaling is a method that uses the median and interquartile range to scale the data. It is less affected by outliers compared to Min-Max scaling and Standardization.

Log Transformation: Log transformation is useful for data that follows a skewed distribution. Applying a logarithmic transformation can help normalize the data and reduce the influence of extreme values.

The choice of data scaling method depends on the characteristics of the data and the requirements of the analysis or modeling task. In machine learning, data scaling is often an essential preprocessing step, especially for algorithms like gradient descent-based optimization methods and distance-based algorithms like k-nearest neighbors.

It is important to note that data scaling should be performed on the training data and then applied consistently to the testing data. This ensures that the testing data is scaled in the same way as the training data, preventing data leakage and ensuring fair evaluation of the model's performance.

Data scaling is a valuable technique that allows for more stable and accurate analysis and modeling, particularly when dealing with datasets with diverse features and scales.

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x_standardized = (x - mean(x)) / std(x)

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x_scaled = (x - min(x)) / (max(x) - min(x))