There is no other way - either rethink representation of your data, or change approach. use methods which are similarity-based (like kNN) and simply define your own measure of what it means that two "lists of numbers" are similar.there are models that can deal with varied length inputs, such as LSTMs, but it is a huge jump from logistic regression to recurrent neural nets.There is no general way of doing that - everything depends on data. I have used the sklearn countvectorizer to create a bag of words but when I try to run it through an sklearn classifier it gives me the ValueError: setting an array element with a sequence. For instance, programmers and developers face this issue when a built-in function or operation gets an argument with the correct type and invalid value. you predefine your own, custom mapping (feature extraction step) that given your varied-length point outputs a fixed length representation (so outputs d numbers). I am building a text classifier and I need some help. The valueerror: setting an array element with a sequence fromiter happens due to different array dimensions and shapes inside the syntax.Check a similar question here in stackoverflow: ValueError: setting an array element with a sequence. What is a typical solution to working with "odd" data: Its probably because '' does not have the same number of elements in all its arrays. There is no "way around this" it is simply a wrong idea. Logistic regression is an estimator for functions of form: R^d -> īut your data clearly is not a subset of R^d, as each sample in a has different length (number of dimensions), thus it cannot be applied.Īnother problem is that p should be a list of samples too, not a single sample (and it has to have d dimensions too, of course). Try using only the training data in the fitness routine, and try expanding out the data and set to have more values. You can’t set the column df‘text’ to a matrix without up the dimensions.
Is there some way to change the data such that I can the apply the classifier and predict the results Tfidfvectorizer returns a 2-Dimension array. ValueError: setting an array element with a sequence. I am getting the following error Traceback (most recent call last):įile "F:\python_3.4\NLP\t.py", line 7, in įile "C:\Python34\lib\site-packages\sklearn\linear_model\logistic.py", line 1173, in fitįile "C:\Python34\lib\site-packages\sklearn\utils\validation.py", line 521, in check_X_yĮnsure_min_features, warn_on_dtype, estimator)įile "C:\Python34\lib\site-packages\sklearn\utils\validation.py", line 382, in check_arrayĪrray = np.array(array, dtype=dtype, order=order, copy=copy)
#Setting an array element with a sequence sklearn code#
The following is my code from sklearn.linear_model import LogisticRegressionĪ =, ,, ]ĬlfModel1 = LogisticRegression(class_weight='balanced') I am new to machine learning, I am trying to apply logistic regression on my sample data set I have a single feature that contains a list of numbers and want to predict class.
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