Feature Scaling for Machine Learning: Standardization and Normalization #machinelearning

Feature scaling is a vital preprocessing step in machine learning, ensuring that numerical features contribute equally to the model's performance. It comes in two main forms: standardization and normalization.

Standardization rescales data to have a mean of 0 and a standard deviation of 1. This method is especially beneficial for algorithms that assume a normal distribution or are sensitive to the scale of input variables, like linear regression or k-nearest neighbors. It involves subtracting the mean from each data point and dividing by the standard deviation.

Normalization, on the other hand, adjusts values to a common scale, typically between 0 and 1. This is crucial for models sensitive to the range of data, like neural networks. It's done by subtracting the minimum value and dividing by the range of the dataset.

Both techniques help in improving model accuracy and efficiency, making them indispensable in a data scientist.

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