I have concatenated two models that uses different inputs. The first model uses input of shape (1, 33). The second model uses a feature set of dimension (1, 1024). I have a mapping function that converts (1, 33) data to (1, 1024). My question is what changes I need to make to make this model work. What is the appropriate way to give test input to this stacked model?

I want to combine some estimators, such as Logistic Regression, Gaussian NB and K-Nearest Neighbors for Features Selection, I tried to use StackingClassifier() estimator to do that, but there is no feature_importances_ attribute for this estimator. Is there any other method to select features combining those classifiers ?? Thank you in advance :)

Stacking can be achieved with heterogeneous algorithms such as RF, SVM and KNN. However, can such heterogeneously be achieved in Bagging or Boosting? For example, in Boosting, instead of using RF in all the iterations, could we use different algorithms?

I have a data set X,y and split them to train and test data. X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, stratify = y, random_state=10). To handle imbalanced data, I wanna use SMOTE and then use classification algorithms. However, I am going to use Stacking as my classification method. I would be thankful to know when I should use SMOTE? Should I use them in defining lower-level classifiers or in higher-level classifiers? level0 = list() oversample = …

How does stacking help in terms of bias and variance? I have a hunch that stacking can help reduce bias but i am not sure, could someone refer to a paper?

Situation 1: I have trained a text classification model (Model 1) which gives me a probability of true class as X. I have also trained a classification model (Model 2) using only the categorical and numeric data. Both the models are used to predict the same true class; just the features differ. I used a random forest classifier on the probabilities returned by Model 1 and Model 2(taking them as input features) and got similar performance metrics(Accuracy, Precision recall). feature …

In stacked generalization, several algorithms (I use some random trees, booster trees, etc.) are first trained and used to make the predictions which are used as input for another algorithm. However, can I use any kind of algorithms, or is there a preference? P.S.: I often see people using linear models in this case.

With stacking, several (diverse) base learners are used to predict the dependent variable $\hat{y}_{b,m}=\beta_{b,m} X$ in a hold-out set, where $m$ are base learner models $1,...,n$. These predictions are used in a second step as explanatory variable(s) in a meta learner $y = \beta_1 X + \beta_2 \hat{y}_b + u$. I wonder how to best treat $\hat{y}_{b,m}$ in practice. There are basically two options: Use each base learner's prediction $\hat{y}_{b,m}$ as a separate feature (column) in the meta learner model. …