Maybe what you are looking for is dimensionality reduction, which will give you a lower-dimensional view of the data. That way you can get a 2d representation of your data which you can plot. You can then color the samples according to the classes or decision values.

You can start with PCA from the decomposition module or try things in the manifold module.

Examples:
http://scikit-learn.org/stable/auto_examples/decomposition/plot_pca_iris.html#example-decomposition-plot-pca-iris-py
http://scikit-learn.org/dev/auto_examples/manifold/plot_lle_digits.html

You should take the results with a good grain of salt, though, as they might not reflect what is really happening in 18 dimensions.



On 11/20/2014 10:33 AM, John Doe wrote:
Thanks for your response Andy.

Is there another application that might help me plot this information on a graph? Really what I am looking for is perhaps to assign a numerical representation for each sample based off of the 18 attributes and then plot that numerical representation on a graph to see which samples are similar to other samples. My math is not strong at all so I am not even sure if that is possible or not.

On Wed, Nov 19, 2014 at 12:20 PM, Andy <[email protected] <mailto:[email protected]>> wrote:

    Hi.

    The example is meant to illustrate the prediction boundaries  on a
    2d example.
    You can not plot the decision boundaries like that for higher
    dimensions.

    Just predicting should work fine, though.

    Hth,
    Andy



    On 11/19/2014 11:56 AM, John Doe wrote:

    I have just started using scikit and I have been following the
    example code at
    
http://scikit-learn.org/stable/auto_examples/neighbors/plot_classification.html


    I am having some trouble with my data, here is the code that I
    have been using to import my data from csv files that I dump out
    of a database.


    query_csv_file = 'query_result.csv'

    X = np.loadtxt(fname = query_csv_file, delimiter = ',', skiprows = 1)

    id_csv_file = 'query_id.csv'

    y = np.loadtxt(fname = id_csv_file, delimiter = ‘,', skiprows = 1)


    query_result.csv looks like the following:

    
attr1,attr2,attr3,attr4,attr5,attr6,attr7,attr8,attr9,attr10,attr11,attr12,attr13,attr14,attr15,attr16,attr17,attr18

    0,797.3700774,0,330.8733288
    
<tel:330.8733288>,155.4552568,0,6177.289831,0,355.3611942,82.92122107,0,49.93354722,1592.292597,435.3831843,721.4373622,73.07706747,194.5707382,0


    query_id.csv looks like the following:

    id

    1


    If I try to run the code in the example using this data I get the
    following error:

    ValueError: Incompatible dimension for X and Y matrices:
    X.shape[1] == 2 while Y.shape[1] == 18


    If I add the following line of code it works but then it is not
    using all 18 attributes:

    X = X[:, :2]


    Any help or guidance would be appreciated! Thanks in advance!



    
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