I have trained LDA model on 2000 URL's(containing articles) on a particular topic in Python3. Can we predict new corpus based on the trained model?
Assuming your dictionary is named as dic_1 and new_corpus is collections of documents.
We first create a gensim corpus by following:
corpus_1= [dic_1.doc2bow(tokenize(doc)) for doc in new_corpus ]
Now we can make prediction using trained model by following:
new_predictions= LDA[corpus_1]
Related
Thanks for your interest and help.
I built a Kernel SVM classifier with 30,000 rows of the training dataset by software R.
I used around 2,000-word features to train the classifier. It worked very well.
But, when I am trying to apply the classifier to a new text dataset, the problem occurred.
Because the new text document-term matrix does not contain all 2000-word features in the classifier (columns).
Of course, I can build a classifier with a small number of word features. Then, it works on the new text data, but the performance is not that good.
How do you solve this problem?
So, how do you solve the problem that the new text dataset does not have all the word features in the SVM classifier?
I asked a question and answer it myself for other users.
I may find the solution.
The problem is that the columns (word-features) in the DTM of the trainset and the unseen dataset are different.
So, when making a DTM of the unseen dataset, use word features of the trainset's DTM as a dictionary.
For example,
features <- trainset_dtm$dimnames$Terms
unseen_dtm <- DocumentTermMatrix(unseen_cropus, control = list(dictionary=features))
Finally, the columns in both dtm(train / unseen) are same. SO, SVM works on the unseen_dtm.
I believe that a Bayesian classifier is based on statistical model. But after training a Bayesian model, I can save it and do not need the training dataset to predict the test data. For example, if I build a bayesian model by
y - labels,X-samples
Can I take the model as a equation like this?
If so, how can I extract the weights and bias? and what is the new formula looks like?If not, what is the new equation like?
Yes, from the docs, a trained classifier has two attributes, intercept_ and coef_ which are useful if you want to interpret the NBC as a linear model.
I am newbie in R and I need to know how to plot a tree selected from a random forest training model created using the train () function in caret package.
First and foremost, I used a training dataset to create a fitting model of a random forest using the train() function. The created random forest contains about 500 trees. Is there any methodology to create a plot of a selected tree?
Thank you.
CRAN package party offers a method called prettyTree.
Look here
As far as I know, the randomForest package does not have any built-in functionality to plot individual trees. You can extract trees using the getTree() function, but nothing is provided to plot / visualize it. This question may be a duplicate as a quick search yielded approaches other people have used to extract trees from a random forest are found
here and here and here
I have created a random forest model for classification. The memory size of the model is too large. I want to use this random forest model to predict class labels for new observations. How can I extract only the rules associated with each tree to save the memory size. A similar question is asked before in here
https://stats.stackexchange.com/questions/102667/reduce-random-forest-model-memory-size.
How to save caret trained models so that it can be used later for building ensemble models in RStudio?