How to normalize close range data?
I use logistic regression. I have some features. Their values are between 0 and 1, (The maximum value that the function can produce is 1 and the minimum value is 0), but both in training and test data the maximum value is very low (e.g. 0.11) therefore all values are low and close to each other. My question is that what is the best standard way to normalize/transfer the feature values to a normal scale (between 0 and 1) so that the logistic regression isn't affected by inappropriate values. Any help would be highly appreciated.
There are different methods for feature scaling/normalization. If you just want the feature values to be in range [0..1] do the following for each feature: Some tutorials recommend to scale features into the range [-0.5 .. 0.5]: I prefer to scale features by their standard deviation how explained in Stanford lectures (see chapter Preprocessing your data):
Multi label classification of reviews
ArcMap conditional statement raster attribute?
WEKA classifier evaluation
KNN giving highest accuracy with K=1?
Ensemble classifier for different features
getting paragraph representation for unseen paragraphs in doc2vec
Does Weka setClassIndex and setAttributeIndices start attribute from different rage?
Criteria to classify retail customers as churn Y or N
How to quantify similarity of tree models? (XGB, Random Forest, Gradient Boosting, etc.)
Logistic Regression(Classification Technique) on Time-dependent Predictors/variables Data
High Relative absolute error and Root relative squared error in classification
voting with average of probabilities in weka
Weka : how to use cross validation in code
Decision Tree relevent classification for this task?
Accuracy of a naive bayes classifier
Weka library java: how to get the prospect of a classification?