Unsupervised Binning
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| Unsupervised binning methods transform numerical variables into categorical counterparts but do not use the target (class) information. Equal Width and Equal Frequency are two unsupervised binning methods. |
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1- Equal Width Binning
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| The algorithm divides the data into k intervals of equal size. The width of intervals is: |
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w = (max-min)/k
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| And the interval boundaries are: |
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min+w, min+2w, ... , min+(k-1)w
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2- Equal Frequency Binning
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| The algorithm divides the data into k groups which each group contains approximately same number of values. For the both methods, the best way of determining k is by looking at the histogram and try different intervals or groups. |
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| Example: |
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