Contents
Why is scaling important in a neural network?
A target variable with a large spread of values, in turn, may result in large error gradient values causing weight values to change dramatically, making the learning process unstable. Scaling input and output variables is a critical step in using neural network models.
Which is the Best Feature selection method Boruta or minimal optimal?
“Boruta is an all relevant feature selection method, while most other are minimal optimal; this means it tries to find all features carrying information usable for prediction, rather than finding a possibly compact subset of features on which some classifier has a minimal error.” -Miron B. Kursa
What are the two main ideas of Boruta?
Boruta is based on two ideas, shadow features and binomial distribution. When using boruta, the features are not evaluated with themselves but with a randomized version of them.
What are the disadvantages of using Boruta?
The main disadvantage is the computation time, while a lot of algorithms can be executed in seconds or milliseconds, boruta’s execution time is measured in hours. This can make it extremely difficult to adjust the parameters as each tune will require significant additional time.
How to use data scaling improve deep learning model stability?
Scaling input and output variables is a critical step in using neural network models. In practice it is nearly always advantageous to apply pre-processing transformations to the input data before it is presented to a network. Similarly, the outputs of the network are often post-processed to give the required output values.
What should the input variables of a neural network be?
— Page 296, Neural Networks for Pattern Recognition, 1995. The input variables are those that the network takes on the input or visible layer in order to make a prediction. A good rule of thumb is that input variables should be small values, probably in the range of 0-1 or standardized with a zero mean and a standard deviation of one.
How are scales related to the problem being modeled?
Differences in the scales across input variables may increase the difficulty of the problem being modeled. An example of this is that large input values (e.g. a spread of hundreds or thousands of units) can result in a model that learns large weight values.