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Visualize sklearn stackingclassifier model pipeline construct

With scikit-learn pipeline we can visualize our pipeline construct. See below screenshot.
I couldn't find similar plotting feature for a sklearn stacking classifier. How can I represent the ensemble model construct with sklearn stacking classifier?

Just like voting classifier, StackingClassifier too could be added as a component of the model pipeline as shown below:

Visualizing k-means and hierarchical clustering for multiclass dataset

I have implemented code for analysing k-means clustering and hierarchical clustering on the following student performance dataset, but have trouble visualising the plots for the clusters.
Since this is a multiclassification dataset, PCA does not work on it, and I am not aware of an alternate method or workaround it.
Dataset link:
https://archive.ics.uci.edu/ml/datasets/Student+Performance

Learning curves for RandomForest

I'm using a RandomForestClassifier for a binary classification problem.
I plotted the following learning curve. Can I say that more training data would benefit this model?
image:
Learning curve for RandomForest Classifier

From the training curve, it is clear accuracy will not improve by adding more instances.

How to structure this dataset for multinomial logistic regression

I have a dataset like the one in the picture.
I need to apply Logistic Regression to this dataset and try to predict the correct result of R.
The problem is that R can assume 3 different values, as you can see in the picture (W,L,D). How can i structure it to work with logistic regression?
I am using python and sklearn logistic regression.

Create a dataframe or numpy ndarray with A,M,D,H, lets say it as x_train and then R into another ndarrary lets name it as y_train, now you can use below
LogisticRegression(multi_class='multinomial',solver ='newton-cg').fit(x_train,y_train)
But based on actual you may need to do some standardization/transform before you fit into a model, you can also refer to this explanation on using iris data set.

Clustering classifiers with sklearn

I have let's say 100 base classifiers trained using BaggingClassifier from sklearn. I know I can cluster data using sklearn (eg using K-Means) but it's only for dataset not for classifiers. Can I cluster classifiers (from BaggingClassifier) into clusters? If I cannot do this using sklearn then are there any other techniques to cluster base classifiers?