Do I have to do normalization on my data if all the features are of the same scale? for example, all the columns are features and each row/sample is the number of occurrences for each feature? And if normalization is required do I need feature-wise or sample-wise normalization?
When to perform Normalization or Standardization in machine learning?
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No, you do not have to do normalization on your data if all your features are on the same scale.
For standardization, you want to check the statistical distribution of your data to make sure they have a standard normal distribution with mean,μ=0 and standard deviation, σ=1; where μ is the mean (average) and σ is the standard deviation from the mean.
You can do this in pandas by calling
.describe()on your data and investigating themeanandstd. If it happens that some features have normal distribution while others don't, you can carry-our sample-wise standardization (on the entire dataset).