In machine learning, two important statistical concepts used to describe the shape of a data distribution are skewness and kurtosis. These measures help us understand how data is spread out and how it might impact model performance using python.
Skewness tells us whether the data is symmetrical or not around the mean. If the distribution has a longer tail on the right, it is positively skewed. If the longer tail is on the left, it is negatively skewed. A skewness value of zero means the data is perfectly symmetrical. Skewed data can affect certain algorithms that assume a normal distribution, so it may need to be transformed or adjusted.
Kurtosis, on the other hand, describes the sharpness or flatness of a distribution’s peak. A distribution with high kurtosis has a sharp peak and heavy tails, which means it’s more prone to outliers. A low kurtosis distribution has a flatter peak and lighter tails, meaning the data is more evenly spread out. A kurtosis value of zero typically indicates a normal distribution.
Understanding skewness and kurtosis is important because they can influence the assumptions and effectiveness of machine learning models. Highly skewed or kurtotic data might require special preprocessing techniques or different types of algorithms to achieve accurate predictions.
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import numpy as np from scipy.stats import skew, kurtosis # Generate a random dataset data = np.random.normal(0, 1, 1000) # Calculate the skewness and kurtosis of the dataset skewness = skew(data) kurtosis = kurtosis(data) # Print the results print('Skewness:', skewness) print('Kurtosis:', kurtosis) |
On executing this code, you will get the following output −
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Skewness: -0.04119418903611285 Kurtosis: -0.1152250196054534 |
The resulting skewness and kurtosis values should be close to zero for a normal distribution.
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