I have a table of 85 predictors, some of which are numerical, logical, ordinal and nominal (hot-one encoded). They are predicting a single finalScore outcome var which ranges from 0 to 1. I'm running a stepwise GLM using:
% model2 = stepwiseglm(predictors, finalScore);
Each predictor's header indicates which of the four types it is and I'm wondering if there is a way to tell the model that there are these different types. This page suggests there is for categoricals but so far I have not found anything within each of the 4 types I have.
Per Generalized Linear Models walk-through
For a table or dataset array tbl, fitting functions assume that these
data types are categorical
Logical
Categorical (nominal or ordinal)
Character array
As long as the data is represented by the appropriate types in the input table, you shouldn't have to specify any further. To ensure this you can typecast nominal with categorical(), ordinal with ordinal(), and logical with logical().
You can specify categorical vs non-categorical with stepwiseglm(...'CategoricalVars',[0 1 0 1 0 0 0 ...]); but if you typecast your input correctly this should be redundant anyways.
Once the model is built, you can verify that categorical variables and ranges are handled appropriately by checking model2.VariableInfo
Related
I am using idx = kmeans(kddcup,5); for kmeans clustering. 145586 records with 41 features of kddcup99, 10% subset of database into 5 clusters, but MATLAB r2017a gives this error:
Kmeans cannot accept complex data!
I loaded a database in MATLAB that has 42 columns instead of 41, which means that the 42nd column is for type of row (attack, normal, ...) and is not a feature, and I don't know if I should keep that 42nd row or delete it.
I don't know if my work is correct or if there is a mistake in that code.
idx = kmeans(X,k) see documentation requires numeric input for X.
Data, specified as a numeric matrix. The rows of X correspond to
observations, and the columns correspond to variables.
If X is a numeric vector, then kmeans treats it as an n-by-1 data
matrix, regardless of its orientation.
Data Types: single | double
You will need to not pass the 42nd column of kddcup to kmeans.
Since you said that kddcup contains (attack,normal,...) are those char? So, what datatype is kddcup?
Regardless it will need to stripped of the 42nd column and possibly converted into a numeric matrix if it isn't already.
I have question please; concerning cross validation, for me the cross-validation is used to find the best parameters.
but I did not understand the role of this function "crossvalind":Generate cross-validation indices, it just takes a data set without model, like in this exemple :
load fisheriris
[g gn] = grp2idx(species);
[trainIdx testIdx] = crossvalind('HoldOut', species, 1/3);
crossvalind() function splits your data in two groups: the training set and the cross-validation set.
By your example:
[trainIdx testIdx] = crossvalind('HoldOut', size(species,1), 1/3); means split the data in species (2/3 in the training set and 1/3 in the cross-validation set).
Supposing that your data is like:
species=[datarow1;datarow2;datarow3;datarow4;datarow5;datarow6] then
trainIdx would be like [1;1;0;1;1;0] and testIdx would be like [0;0;1;0;0;1] meaning that from the 6 total elements in our set crossvalind function assigned 4 to the train set and 2 to the cross-validation set. Of course this is a random assignment meaning that the zero and ones indices will vary every time you call the function but the proportion between them will be fixed and trainIdx + testIdx will always be ones(size(species,1),1)
crossvalind('LeaveMout',size(species,1),2) would be exactly the same as crossvalind('HoldOut', size(species,1), 1/3) in this particular case. In the 'HoldOut' format you provide parameter P which takes values from 0 to 1 (like 1/3 in the example above) while with the option 'LeaveMout' you provide integer M like 2 samples from the 6 total or like 2000 samples from the 10000 total samples in your dataset. In case of 'Resubstitution': crossvalind('Resubstitution', size(species,1), [1/3,2/3]) would be yet the same but here you also have the option of let's say [1/3,3/4] meaning that some samples can be on both the train and cross-validation sets, or even [1,1] which means that all the samples are used in both sets (trainIdx=testIdx=[1;1;1;1;1;1] in the above example). I strongly suggest to type help crossvalind and take a look at the help file which is always a lot more detailed and helpful than i could ever be.
Hi I have been using Murphy's HMM toolbox with output of Gaussian Mixture. In brief, I have 2 datasets for training. Each dataset comprises of 2000 observations with 11 dimensions per observation. I implemented the following steps to observe the path sequence output.
N_states=2
N_Gaussian_Mixture=1
For each of the dataset, a HMM model was generated. The steps are:
Step 1: mixgauss_init() was used to generated GMM signature for my training data.
Step 2: After declaring the matrices for Prior and Transmat, mhmm_em() was used to generate HMM model for the training dataset.
Testing: 2 test data from each of the dataset are used for testing using mhm_logprob(). The output were correctly predicted using loglikelihood scores in every run.
However, when I tried to observe the sequence of the HMM modelling (Dataset_123 with testdata_123) via mixgauss_prob() followed by viterbi_path(), the output sequences were inconsistent. For example, for the first run, the output sequence can be 2221111111111. But when I rerun the program again, the sequence can change to 1111111111111 or 1111111111222. Initially I thought it could be due to my Prior matrix. I fixed the Prior value but it is not helping.
Secondly, it there a possibility when I can assigned labels to the states and sequence? Like Matlab function:
hmmgenerate(...,'Symbols',SYMBOLS) specifies the symbols that are emitted. SYMBOLS can be a numeric array or a cell array of the names of the symbols. The default symbols are integers 1 through N, where N is the number of possible emissions.
`hmmgenerate(...,'Statenames',STATENAMES) specifies the names of the states. STATENAMES can be a numeric array or a cell array of the names of the states. The default state names are 1 through M, where M is the number of states.?
Thank you for your time and hope to hear from the expert sharing.
I am trying to understand how simple K-means in Weka handles nominal attributes and why it is not efficient in handling such attributes.
I read that it calculates modes for such attributes. I want to know how the similarity is calculated.
Lets take an example:
Consider a dataset with 3 numeric and a nomimal attribute.
The nominal attribute has 3 values: A, B and C.
Instance1 has value A, Instance2 has value B and Instance3 has value A.
In this case, Instance1 may be more similar to Instance3(depending on other numeric attributes of course). How will Simple K-means work in this case?
Follow up:
What if the nominal attribute has more(10) possible values?
You can try to convert it to binary features, for each such nominal attribute, e.g. has_A, has_B, has_C. Then if you scale it i1 and i3 will be closer as the mean for that attribute will be above 0.5 (re to your example) - i2 will stand out more.
If it has more, then you just add more binary features for every possible value. Basically you just pivot each nominal attribute.
I have to write a script using MATLAB which will classify my data.
My data consists of 1051 web pages (rows) and 11000+ words (columns). I am holding the word occurences in the matrix for each page. The first 230 rows are about computer science course (to be labeled with +1) and remaining 821 are not (to be labeled with -1). I am going to label few part of these rows (say 30 rows) by myself. Then SVM will label the remaining unlabeled rows.
I have found that I could solve my problem using MATLAB's svmtrain() and svmclassify() methods. First I need to create SVMStruct.
SVMStruct = svmtrain(Training,Group)
Then I need to use
Group = svmclassify(SVMStruct,Sample)
But the point that I do not know what Training and Group are. For Group Mathworks says:
Grouping variable, which can be a categorical, numeric, or logical
vector, a cell vector of strings, or a character matrix with each row
representing a class label. Each element of Group specifies the group
of the corresponding row of Training. Group should divide Training
into two groups. Group has the same number of elements as there are
rows in Training. svmtrain treats each NaN, empty string, or
'undefined' in Group as a missing value, and ignores the corresponding
row of Training.
And for Training it is said that:
Matrix of training data, where each row corresponds to an observation
or replicate, and each column corresponds to a feature or variable.
svmtrain treats NaNs or empty strings in Training as missing values
and ignores the corresponding rows of Group.
I want to know how I can adopt my data to Training and Group? I need (at least) a little code sample.
EDIT
What I did not understand is that in order to have SVMStruct I have to run
SVMStruct = svmtrain(Training, Group);
and in order to have Group I have to run
Group = svmclassify(SVMStruct,Sample);
Also I still did not get what Sample should be like?
I am confused.
Training would be a matrix with 1051 rows (the webpages/training instances) and 11000 columns (the features/words). I'm assuming you want to test for the existence of each word on a webpage? In this case you could make the entry of the matrix a 1 if the word exists for a given webpage and a 0 if not.
You could initialize the matrix with Training = zeros(1051,11000); but filling the entries would be up to you, presumably done with some other code you've written.
Group is a 1-D column vector with one entry for every training instance (webpage) than tells you which of two classes the webpage belongs to. In your case you would make the first 230 entries a "+1" for computer science and the remaining 821 entries a "-1" for not.
Group = zeros(1051,1); % gives you a matrix of zeros with 1051 rows and 1 column
Group(1:230) = 1; % set first 230 entries to +1
Group(231:end) = -1; % set the rest to -1