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How to Use Statistical Significance Tests to Interpret Machine Learning Results

How to Use Statistical Significance Tests to Interpret Machine Learning Results

It is good practice to gather a population of results when comparing two different machine learning algorithms or when comparing the same algorithm with different configurations. Repeating each experimental run 30 or more times gives you a population of results from which you can calculate the mean expected performance, given the stochastic nature of most […]

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Zoomed Line Plot of Mean Result with Standard Error Bars and Population Mean

Estimate the Number of Experiment Repeats for Stochastic Machine Learning Algorithms

A problem with many stochastic machine learning algorithms is that different runs of the same algorithm on the same data return different results. This means that when performing experiments to configure a stochastic algorithm or compare algorithms, you must collect multiple results and use the average performance to summarize the skill of the model. This […]

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crash course in statistics

Crash Course in Statistics for Machine Learning

You do not need to know statistics before you can start learning and applying machine learning. You can start today. Nevertheless, knowing some statistics can be very helpful to understand the language used in machine learning. Knowing some statistics will eventually be required when you want to start making strong claims about your results. In […]

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