We Keep Coding

3D dataset and sklearn regression algorithms

I have a dataset similar to the image below.
How can I train one of the regression algorithms defined in the sklearn library using this dataset?

How to make naive bayes multinomial with TF-idf from scratch in python?

I know there is a library in python
from sklearn.naive_bayes import MultinomialNB
but I want to know how to create one from scratch without using libraries like TfIdfVectorizer and MultinomialNB?

Here is the step-by-step about how to make simple MNB Classifier with TF-IDF
First, you need to import the method TfIdfVectorizer to tokenize the terms inside the dataset, the MultinomialNB as the classifier, and the train_test_split for splitting the dataset. (Both are available in sklearn).
Split the dataset into train and test sets.
Initialize the constructor of TfIdfVectorizer, then Vectorize/Tokenize the train set by the method fit_transform.
Vectorize/Fit the test set with the method fit.
Initialize the classifier by calling the constructor MultinomialNB().
model = MultinomialNB() # with default hyperparameters
Train the classifier with the train set.
model.fit(X_train, y_train)
Test/Validate the classifier with the test set.
model.predict(X_test, y_test)
Those 7 steps above are the simple steps. Apparently you can also do the text preprocessing and also model evaluation.

How can I explain predictions of an imblearn pipeline?

I have an imblearn (not sklearn) pipeline consisting of the following steps:
Column selector
Preprocessing pipeline (ColumnTransformer with OneHotEncoders and CountVectorizers on different columns)
imblearn's SMOTE
XGBClassifier
I have a tabular dataset and I'm trying to explain my predictions.
I managed to work out feature importance plots with some work, but can't get either
eli5 or lime to work.
Lime requires that I transform the data to the state of before the last transformation (because the transformers in the Pipeline like custom vectorizers create new columns).
In principle, I can slice my Pipeline like this: pipeline[:-1].predict(instance). However, I get the following error: {AttributeError}'SMOTE' object has no attribute 'predict'.
I also tried an eli5 explainer, since it supposedly works with Sklearn Pipelines.
However, after running eli5.sklearn.explain_prediction.explain_prediction_sklearn_not_supported(pipeline, instance_to_explain) I get the message that the classifier is not supported.
Will appreciate any ideas on how to proceed with this.

Imblearn's samplers are effectively no-op (ie. identity) transformers during prediction. Therefore, it should be safe to delete them after the pipeline has been fitted.
Try the following workflow:
Construct an Imblearn pipeline, and fit it.
Extract the steps of the fitted Imblearn pipeline to a new Scikit-Learn pipeline.
Delete the SMOTE step.
Explain your predictions using standard Scikit-Learn pipeline explanation tools.

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?

Python RandomForest classifier (how to test it)

I have been able to create a RandomForestClassifier on a dataset.
clf = RandomForestClassifier(n_estimators=100, random_state = 101)
I can then use it on the test data like this:
prediction = pd.DataFrame(clf.predict(x)) # x = Matrix of predictor values
So my question is, how can I test clf.predict outside of Python, how can I see the values that is using and how can I test it "manually" for example if you get the betas in a Regression you can then use those values in Excel and replicate the model. How to do this with RandomForests in Python?
Also is there a similar metric to Rsquared to test the model's explication power?
Thanks!

The RandomForestClassifier is an ensemble of trees which means it is composed by multiple trees.
To be able to test the trees I would suggest to do it in Python itself, you can access all the trees in the estimators_ attribute of the classifier and subsequently export them as graphs with export_graphviz from sklearn.tree module.
If you insist on exporting the trees you will need to export all the rules that each tree is composed by. For that, you can follow this instructions from the sklearn docs.
Regarding the metrics, for a classification problem you could use accuracy_score from sklearn.metrics module.