How can I solve a classification problem with a dependent variable with more than two values
multi target regression
multinomial logistic regression in r
multivariate multiple regression
multivariate regression correlated dependent variables
logistic regression with more than 2 levels
multiple dependent variables in a model
multivariate logistic regression
I have a simple NLP problem, where I have some written reviews that have a simple binary positive or negative judgement. In this case I am able to train and test as independent variables the columns of X that contain the "bags of words", namely the single words in a sparse matrix.
from sklearn.feature_extraction.text import CountVectorizer cv = CountVectorizer(max_features = 300) #indipendent X = cv.fit_transform(corpus).toarray() #dependent y = dataset.iloc[:, 1].values
..and the dependent variable y, that is represented by the column 1 that assume values as 0 and 1( so basically positive and negative review).
if instead of 0 and 1, I have reviews that can be voted from 1 to 5 stars should I proceed having an y variable column with values from 0 to 4?In other words I would lie to know how differ the model if instead of a binary good/bad review, the user has the possibility after his or her review to give a rating from 1 to 5. How is called this kind of problem in machine learning?
It is just multi-class classification problem. Here is a sample code from where you can get an idea. What you are calling 'dependent variable' is called class (class that the input example belongs to)
label_idx = [unique.index(l) for l in labels] """ labels= class. works for your class is string or so. here labels can be more than two""" label_idx = np.array(label_idx) # just get your class into array vectors = np.array(vecs) # vecs are any vectorised form of your text data clf = LinearSVC() # classifier of your choice clf.fit(vectors, label_idx)
How to use logistic regression analysis for more than two class , A dependent variable. can take only two values. My question, can we use logistic regression analysis for more than two class problem?? Logistic Regression. As Geoffery told u can use multinomial logit. if the values of dependent value reflect order (eg 3 best, 2 - better, 1 good) use ordinal logit. but be careful in interpretation as it differs based
I have used the following link for a RandomForest multiClassifier which is one of many possible ML algorithms you can use:
However, my personal experience shows deep learning neural networks work better with "text data" and tree-based models are better for tabular data with numeric values.
How to deal with multiple dependent variables?, in your case dependent variable has three factor: a,b,c then the value of dependant If the dependent variable has more than two factors you can use either order solution, then this indicate that the set of items are measuring more than one Can you perform a multiple regression with two dependent variables? Question. Logistic Regression is one of the basic and popular algorithm to solve a classification problem. It is named as ‘Logistic Regression’, because it’s underlying technique is quite the same as Linear Regression. The term “Logistic” is taken from the Logit function that is used in this method of classification.
This problem is called as multi-class classification problem as mentioned by @rishi. There is a large variety of algorithms that can solve the multi-class problem. Look here
You could make your target variable as one, which as the ratings.
#dependent y = dataset.iloc[:, 'ratings'].values
Then, you can fit this data into the classifier!
from sklearn import linear_model clf = linear_model.SGDClassifier() clf.fit(X, y)
Regression Models with multiple target variables, For classification models, a problem with multiple target variables is called variable which is based on the value of two or more independent variables. Therefore, each instance can be assigned with multiple categories, so these types of problems are known as multi-label classification problem, where we have a set of target labels. Great! Now you can distinguish between a multi-label and multi-class problem.
Integrated Computer Technologies in Mechanical Engineering: , Much attention is paid to the classification problems of complex systems. for solving the classification problem for technical and biomedical systems. two levels of the dependent variable: categorical output data with more than two values Binary Classification: Classification task with two possible outcomes. Eg: Gender classification (Male / Female) Multi-class classification: Classification with more than two classes. In multi class classification each sample is assigned to one and only one target label. Eg: An animal can be cat or dog but not both at the same time
Keras to Kubernetes: The Journey of a Machine Learning Model to , We can also manually solve this using the preceding equation for Area = 95 and high error values with linear models, then usually we need to start looking at more In classification, your outcome or dependent variable is not a value but a is a binary classification problem and the output variable can have one of two If more than one independent variable is available, then this is called multiple linear regression. This formula is employed to estimate real values like the price of homes, number of calls, total sales based on continuous variables.
Numerical Methods for Engineers and Scientists, Many practical problems involve several dependent variables, each of which comprise a system of two coupled first-order ordinary differential equations. The general solution of a differential equation contains one or more constants of integration. CLASSIFICATION OF ORDINARY DIFFERENTIAL ECUATIONS Physical One option is to do as you mention, create three models, one for each dependent variable. You can linearly combine the three DVs (e.g., sum or average), if that makes sense for your research question. Then fit one model for that new DV.
- Have you looked into multiclass? scikit-learn.org/stable/modules/multiclass.html
- thanks, now I learned a lot with all these replies. I am going to upvote the others and vote for @Rishi