normaaliinastamisen

Normaaliinastaminen, also known as normalization or standardization, is a data preprocessing technique used to scale numerical attributes in a dataset. The goal is to transform the data so that it has a mean of zero and a standard deviation of one. This is achieved by subtracting the mean of the feature from each data point and then dividing by the standard deviation of that feature.

This process is crucial in many machine learning algorithms, especially those that are sensitive to the scale of input features. Algorithms like gradient descent, support vector machines, and principal component analysis often perform better and converge faster when the data is normalized. Features with larger ranges can otherwise dominate those with smaller ranges, leading to biased results or suboptimal performance.

The formula for z-score normalization is as follows: z = (x - μ) / σ, where x is the original data point, μ is the mean of the feature, and σ is the standard deviation of the feature. The resulting z-scores indicate how many standard deviations a data point is away from the mean.

Another common normalization technique is min-max scaling, which scales data to a fixed range, typically between 0 and 1. The formula for min-max scaling is: x_scaled = (x - min) / (max - min). This method is useful when you want to preserve the relationships between data points but want them within a specific bounded interval.

Choosing between different normalization methods depends on the specific algorithm and the characteristics of the data. However, normalization in general helps to ensure that all features contribute equally to the model's learning process, improving its accuracy and robustness.