For reduce the dimension of our studies , I will use a Principal Component Analysis. Normally this method permits to decrease considerably the number of variables in changing the dimensions. In clear the dimensions discovered give new and compacted variables and are not the same as compared with our first values. The new axes represent a part of most than one variable.
This new dimension can be used in a Perceptron to restrict to the most important variables our study and at the same time to reduce the time to calculate, to test and to validate a model.
The first thing to do , to understand this task, is to see the compression level of each of our axes.
The figure give this information:
We represent the inertia of the compression representation of our variables.
We can see that 20 first axes permit to cover more than 85% of our variables.
The three first axes give more than 50% of variables representation.
This new dimension can be used in a Perceptron to restrict to the most important variables our study and at the same time to reduce the time to calculate, to test and to validate a model.
The first thing to do , to understand this task, is to see the compression level of each of our axes.
The figure give this information:
We represent the inertia of the compression representation of our variables.
We can see that 20 first axes permit to cover more than 85% of our variables.
The three first axes give more than 50% of variables representation.
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