query: How to normalize data to let each feature lie between [-1,1]?
positive_passages.docid: 21130
positive_passages.text: How to normalize data between -1 and 1?
negative_passages.docid: 112942
negative_passages.text: Checking Normality of Numerical, and Categorical Data
negative_passages.docid: 7695
negative_passages.text: Managing Exclusions in Plot[ ]
negative_passages.docid: 31680
negative_passages.text: How can I group numerical data into naturally forming "brackets"? (e.g. income)
negative_passages.docid: 64557
negative_passages.text: Which constant to add when applying 'Box-Cox transformation' to negative values?
negative_passages.docid: 8703
negative_passages.text: How to apply standardization/normalization to train- and testset if prediction is the goal?
negative_passages.docid: 115931
negative_passages.text: Assign p-values to all data point in the population in order to demonstrate some of them are not normal
negative_passages.docid: 86813
negative_passages.text: Correct alignment of multiple-line inequality
negative_passages.docid: 23868
negative_passages.text: Normality assumption in linear regression
negative_passages.docid: 129169
negative_passages.text: Transforming positively skewed data with positive and negative values
negative_passages.docid: 78236
negative_passages.text: How to perform imputation of values in very large number of data points?
negative_passages.docid: 90969
negative_passages.text: De normalize predicted value
negative_passages.docid: 35824
negative_passages.text: How can i reduce the linear fit plot to a certain interval?
negative_passages.docid: 77851
negative_passages.text: Should we apply normalization to test data as well?
negative_passages.docid: 152239
negative_passages.text: Dataset collapsing/reducing
negative_passages.docid: 34949
negative_passages.text: Generating a high-dimensional dataset where nearest neighbor becomes meaningless
negative_passages.docid: 9663
negative_passages.text: Is it a good practice to always scale/normalize data for machine learning?