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If I understand correctly, you want a sample with along each feature the value below which a given percentage of observations in your population falls. If so, you might want to try something like this using np.percentile: import numpy as np import pandas as pd data = [ np.random.randint(1,10,6) for i in range(20) ] # fake data df = pd.DataFrame(data=data, ...

-2

An early thought on scaling led to postulating norms that produced different categories for individuals. Different scales e.g Likert Scale emerged for measuring various concepts. Weighing procedures were conceived to measure the scale components by using the normal distribution theory.The developments in mathematical sciences helped refinement of Scales. ...

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The effect will be increasing the intercept. I don't recommend doing oversampling unless any other solution doesn't work. Besides, 10% is not such a big imbalance. I've been in kaggle competitions with way more imbalance where no imbalance solutions were adopted, logistic regression and random forest work quite well without the need of these. Edit After @...

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The terms standardization and normalization are often used interchangeably. However, strictly speaking they do refer to distinct feature transformations. Normalization Normalization, also called feature scaling usually means scaling the data between 0 and 1. There are many approaches that can be used to achieve this. One common way is by \$x' = \frac{x - x_{...

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