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So, to me what you have to do is : Transform all your your True/False to 1/0, so they're numerical. Keep age as it is (or use some normalisation, but not that necessary Absolutely change the way Sex is handled. You have a big bias since you have 3 values : Since it's numerical, distance matters. Here, distance between "Male" and "Diverse&...


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How to Standardize Image With ImageDataGenerator Standardization is a data scaling technique that assumes that the distribution of the data is Gaussian and shifts the distribution of the data to have a mean of zero and a standard deviation of one. Data with this distribution is referred to as a standard Gaussian. It can be beneficial when training neural ...


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The data description says: Note: Each of these 10 feature variables have been mean centered and scaled by the standard deviation times n_samples (i.e. the sum of squares of each column totals 1). https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html For more information see: Bradley Efron, Trevor Hastie, Iain Johnstone and Robert Tibshirani (2004) "...


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The goal of supervised machine learning is automatically learn the features weights to predict target values. If you have target values, you can fit a machine learning algorithm (e.g., a k-nearest neighbors or a neural networks). There is no need to pick the weights yourself, the algorithm will do it for you.


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Both are linear transformations. In general you should try both and see which performs better. MinMaxScaler: Has the problem that your features will not have the same range of values after scaling. The advantage is that you might have intrinsic boundaries for your features. Also the interpretation of your variables ist still pretty straight forward. ...


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