Quantile Transformation is a statistical technique used to map data to a uniform distribution by transforming each feature to follow a specified distribution, often a normal distribution. This method is particularly useful in machine learning to reduce the impact of outliers and ensure that different features have similar scales, improving model performance and convergence.

Quantile Transformation

statistical technique

Uniform distribution is a probability distribution where all outcomes are equally likely within a defined range, characterized by a constant probability density function. It is crucial in simulations and modeling when each outcome within the interval is assumed to have the same likelihood of occurring.

uniform distribution

The normal distribution, also known as the Gaussian distribution, is a continuous probability distribution characterized by its symmetrical bell-shaped curve, where the mean, median, and mode are all equal. It is fundamental in statistics because many natural phenomena and measurement errors are approximately normally distributed, making it a cornerstone for statistical inference and hypothesis testing.

normal distribution

Machine learning is a subset of artificial intelligence that involves the use of algorithms and statistical models to enable computers to improve their performance on a task through experience. It leverages data to train models that can make predictions or decisions without being explicitly programmed for specific tasks.

machine learning

impact of outliers

different features

similar scales

Model performance refers to the effectiveness of a machine learning model in making accurate predictions or classifications based on the data it was trained on. It is typically evaluated using various metrics that assess how well the model generalizes to new, unseen data.

model performance

Model convergence refers to the process where a machine learning model's training stabilizes, achieving minimal error rates and optimal performance on the given task. It indicates that the model has effectively learned the underlying patterns in the data and further training will not significantly improve its accuracy.

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