predictive | Simple library

I have some high dimensional repeated measures data, and i am interested in fitting random forest model to investigate the suitability and predictive utility of such models. Specifically i am trying to implement the methods in the LongituRF package. The methods behind this package are detailed here :

Capitaine, L., et al. Random forests for high-dimensional longitudinal data. Stat Methods Med Res (2020) doi:10.1177/0962280220946080.

Conveniently the authors provide some useful data generating functions for testing. So we have

I want to pass my predictive model values that will be extracted from Pandas DataFrame based on condition and results from models will be placed in Pandas DataFrame.

DataFrame

I am trying to create a decision tree based on some training data. I have never created a decision tree before, but have completed a few linear regression models. I have 3 questions:

1. With linear regression I find it fairly easy to plot graphs, fit models, group factor levels, check P statistics etc. in an iterative fashion until I end up with a good predictive model. I have no idea how to evaluate a decision tree. Is there a way to get a summary of the model, (for example, .summary() function in statsmodels)? Should this be an iterative process where I decide whether a factor is significant - if so how can I tell?

2. I have been very unsuccessful in visualising the decision tree. On the various different ways I have tried, the code seems to run without any errors, yet nothing appears / plots. The only thing I can do successfully is tree.export_text(model), which just states feature_1, feature_2, and so on. I don't know what any of the features actually are. Has anybody come across these difficulties with visualising / have a simple solution?

3. The confusion matrix that I have generated is as follows:

   ...

I have trained a doc2vec (PV-DM) model in gensim on documents which fall into a few classes. I am working in a non-linguistic setting where both the number of documents and the number of unique words are small (~100 documents, ~100 words) for practical reasons. Each document has perhaps 10k tokens. My goal is to show that the doc2vec embeddings are more predictive of document class than simpler statistics and to explain which words (or perhaps word sequences, etc.) in each document are indicative of class.

I have good performance of a (cross-validated) classifier trained on the embeddings compared to one compared on the other statistic, but I am still unsure of how to connect the results of the classifier to any features of a given document. Is there a standard way to do this? My first inclination was to simply pass the co-learned word embeddings through the document classifier in order to see which words inhabited which classifier-partitioned regions of the embedding space. The document classes output on word embeddings are very consistent across cross validation splits, which is encouraging, although I don't know how to turn these effective labels into a statement to the effect of "Document X got label Y because of such and such properties of words A, B and C in the document".

Another idea is to look at similarities between word vectors and document vectors. The ordering of similar word vectors is pretty stable across random seeds and hyperparameters, but the output of this sort of labeling does not correspond at all to the output from the previous method.

Thanks for help in advance.

Edit: Here are some clarifying points. The tokens in the "documents" are ordered, and they are measured from a discrete-valued process whose states, I suspect, get their "meaning" from context in the sequence, much like words. There are only a handful of classes, usually between 3 and 5. The documents are given unique tags and the classes are not used for learning the embedding. The embeddings have rather dimension, always < 100, which are learned over many epochs, since I am only worried about overfitting when the classifier is learned, not the embeddings. For now, I'm using a multinomial logistic regressor for classification, but I'm not married to it. On that note, I've also tried using the normalized regressor coefficients as vector in the embedding space to which I can compare words, documents, etc.

I am trying to create a predictive model using linear regression with a dataset that has 157673 entries.

The data (in a csv file) is in such format:

Hope all of you guys are healthy and well. I am new to the world of NLP and my question may sound stupid, so I apologize in advance.I would like to perform NLP on some text data which is labeled and run a text mining predictive model. I have four text columns that can be used as predictors and my labeled column is my class variable. Perhaps, the following can give you a glimpse of the data set

I am using spring boot with spring data mongo and @Query to carry out a regex expression query also using spell.

In my documents I have something like this

I want to be able to filter also by a predictive string depending on the language (don't worry about languages my main problem is in the regex)

I am very new to Python and coding and I am working on a predictive model for a Kaggle Prediction Competition. I am trying to write code to delete a certain variable that I deemed nonimportant for predicting the survivability of the sinking of the Titanic (the Kaggle Competition prompt). FYI, 'Cabin' is a defined term because it is a variable and a part of the information given.

My code is:

I have a training data (train.dat) and test data (test.dat). I would like to run my LASSO model on the test data after training it on the training data, which seems to have gone ok.

From there, I would like to get the RMSE and R2 to observe the predictive accuracy of the model. However, I get the errors: Error in pred - obs : non-numeric argument to binary operator (for RMSE) and Error in complete.cases(pred) : not all arguments have the same length for R2.

Can anyone tell me what has gone wrong with my code?