Questions tagged [stacking]

Stacking is a meta-ensemble machine learning technique that trains a second-level machine learning model on the predictions from multiple machine learning models trained on the data.

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Ensemble Methods for Probabilities

I am currently trying to build a stacked algorithm in order to determine how many people in each region of a country will be likely to buy a product versus its competitors. I have some data from an ...
huntercallum's user avatar
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Why would a model combining two pre-trained models not even achieve the performance of the best sub-model?

I have two different CNNs trained on the same dataset. One performs a bit better than the other but I believe each can provide different and useful information. I use ...
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Building a hybrid model? From a Random Forest and a OLS linear regressions

Cureently, I am conducting a regression study of household expenditure (target variable) from a set of determiants (income, household size, ...) in Malaysia using OLS and Random Forest. It is a long ...
Lu Cas's user avatar
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Superlearner Without OOB Results

I'm interested in creating a superlearner algorithm. Unfortunately, my situation is such that I have access to the predictions of submodels I'm interested in on new data, but don't necessarily have ...
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Stacking, adjust the probabilities to new data should improve accuracy?

I'm looking for some expert opinion on what to expect when using the output from model A (probabilities) as input into model B. I have a population where all instances have been assigned a probability ...
Henri's user avatar
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Inherit a model to build a new model

I have the following problem. I have a model, model A. It classifies a persons ability to buy a product. It's a Logistic Regression model. I want to add a new feature, but if I add it to model A ...
Henri's user avatar
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How to evaluate stacked classifiers

I have 2 sets of classifiers, both trained on 2 different feature sets extracted from the same data. I would like to combine them using the "stacking" method, which I understand as follows: ...
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Any differences between Super-learner vs. Stacked generalizations vs. Stacked regressions?

I'm trying to figure out the differences between the "Super Learner" approach of Van der Laan et al. (2007), the "Stacked regressions" approach of Leo Breiman (1996) and the "...
Björn's user avatar
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How to deal with a regression where a subset of data has more target information

I am regressing the hire price of venues. I have a dataset with lots of venues containing information on each venue. Each row is one venue and an associated price and an associated day of week from ...
Rupert Hart's user avatar
2 votes
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Understanding Stacked Generalization

I am trying to figure out how stacked generalization works? I think we train n models on the same dataset and get their class probabilities. Then these class probabilities are fed into another model. ...
deniyore's user avatar
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Is data leakage a concern when using an ensemble of leave-one-out predictions?

I am new to stacking. I have a dataset with N samples and 7 tables corresponding to different data types, plus a binary label. Some tables have dozens of features, other have many thousands. I train ...
SebDL's user avatar
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Ensemble classifiers trained using different sets of features

Background I have a dataset, each record in this set is represented by 2 different sets of features. Let's say feature set A and feature set B. I have trained classifiers using feature set A and ...
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Power calculation to check if multiple predictive models are correlated

Say there is a classification setting so that $$ { \{(x_1,y_1),(x_2,y_2), ..., (x_n,y_n)\} } $$ is a set of $n$ observations, and then there is a set of $m$ estimators (models) that take an individual ...
Elabore's user avatar
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Outputs from models (trained on different data) as inputs to another model?

Let's say I want to detect new species of fish. I have several models, each trained to recognize a different characteristic, e.g., the speed of known fish, the size of known fish, their known shapes, ...
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How to find the optimal coefficients of the two predict_proba output matrices of two different classifiers using regression and maximizing accuracy? [closed]

I am performing classification, where there are six labels and two predict_proba (predicted probabilities) matrices as outputs. These two predict_proba matrices correspond to the outputs of two ...
cemrifki's user avatar
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How is loss propagated in stacked classifiers?

In a 2-stage stacked classifier the first model takes the input data and outputs feature vectors, which are then fed into a second model as input. The second model learns the mapping between the ...
xojfqa's user avatar
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Training twice on non-injective data

I have a large dataset of 30000 points, but most my Ys are the same while all Xs are different. Ys are from different samples, so I had means of Y for each sample and I used means alongside Xs to ...
Shayan Kabiri's user avatar
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Why is my stacking/meta-learning not outperforming the best base model?

I have a dataset of around 10,000 rows, with 500 features, response variable is binary classification. I split the features into 5 equally sized groups (based on subject matter expertise), and trained ...
Vladimir Belik's user avatar
3 votes
3 answers
817 views

Why did meta-learning (or model stacking) underperform the individual base learners? [closed]

I want to use meta-learning, specifically, stacking to combine the results of two algorithms, denoted here A and B. The results of A and B correspond to the first and second columns in the dataset '...
tunar's user avatar
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Whether can stacked generalization (stacking) further improve the performance if learner A significantly outperform B?

We know that stacking is the most popular meta-learning technique. It learns from the predictions of the base learners that learn from the training dataset. Now assuming there are two base learners, ...
tunar's user avatar
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SHAP for stacking classifier

We are using a stacking classifier to solve a classification problem. The data feed 5 base models, the predicted probabilities of the base models feed the supervisory classifier. We would like to use ...
donlelef's user avatar
1 vote
1 answer
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Ensemble Model using Stacking

I learned that building an ensemble model using stacking is done by training a meta-model on the predictions of $n$ other models in order to combine the predictions and try to enhance the performance. ...
Mohy Mohamed's user avatar
2 votes
0 answers
363 views

Model Stacking and tuning a meta-model - CV strategy?

I was hoping some of the more experienced ensemblers could help me with a couple of questions I have regarding stacking. The assumption is that we have a classic ...
Liam Morgan's user avatar
2 votes
2 answers
492 views

is it pointless to stack models of different data types/structure?

I am studying ensemble methods machine learning, in particular I am focusing on stacking. In stacking different models are used to get an output. Then all the outputs are 'combined' together to build ...
Alessandro Peca's user avatar
2 votes
1 answer
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Single or multiple model. How to know beforehand?

I am building a probability of default model based on behavioral information. The dataset is a loan portfolio, which contains 4 types of loans: mortgage, unsecured loans, car loans and credit cards. ...
Serge Kashlik's user avatar
3 votes
2 answers
632 views

Why in the stacking of scikit-learn the estimators are fitted on the whole training data?

In chapter 7 of "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow", the first step of stacking method is spliting the train data into two subsets. The first subset is used ...
wutao's user avatar
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StackingClassifier: Different Feature Sets

I have a few different feature sets (so, with same number of rows and the labels are the same) that I use for different ML models. In my case these sets are DataFrames. I want to use them to train a ...
Bow's user avatar
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3 votes
1 answer
588 views

Fitting Stacking Classifer when Underlying Models Use Different Feature Subsets

I’m looking to create a stacking classifier based on 3 underlying algorithms. I have already performed feature selection on each of the 3, and each returned a slightly different subset of features for ...
AMJ's user avatar
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If you want to train a model on the predictions of another trained model, would you use the same training set for both model?

Imagine I train a model and make predictions on it, denoting the training data by x_train and the predictions by y_pred_train. Then, I want to use those predictions in a second ML model. Would you use ...
Tom's user avatar
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3 votes
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Score-probability obtained from Random Forest

Assume we have a classification problem with two classes $\{-1,1\}$. In my research I rather need probability, than just predicted classes. I use isotonic regression to calibrate classifier. In ...
ABK's user avatar
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Is it a good idea to use a linear model (like logistic regression) to generate new features for a non linear model (like random forest)? [duplicate]

The setting is a 2-class classification problem. We have too many features, some of them not very informative and with many zeros. We are thinking in ways of selecting the best features, and PCA (in ...
Jaime Arboleda Castilla's user avatar
1 vote
1 answer
68 views

How to detrend in a stacked model?

Let's say I have user data (x) for customers (i) in the period between 2010 and 2020 (t). I want to predict if customer i churns at time t. To do so, I have built a stacked model which looks somewhat ...
Michieldo's user avatar
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9 votes
1 answer
522 views

AIC model averaging when models are correlated

AIC model-averaging: In "standard" AIC model averaging we average models with weights proportional to $$w_i \propto \exp( -0.5 \times \Delta \text{AIC}_i ),$$ where $\Delta \text{AIC}_i$ is ...
Björn's user avatar
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Model stacking with windowed features - info leakage?

This is similar to other questions on leakage (for example, this post), but all of my data are generated with look-back features, and nothing can be assumed to be iid. I'm curious if you think there ...
Frank Fineis's user avatar
0 votes
1 answer
465 views

StackingClassifier + RandomSearchCV: How is it dividing the folds under the hood?

I'm able to (based on the example from the accepted answer here ) set up a StackedClassifier and add RandomSearchCV to perform a quick hyperparameter search. The models/pipelines are set up like in ...
ednaMode's user avatar
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1 vote
2 answers
182 views

How to combine outputs from different non-machine learning prediction models?

I have a list of cancer mutations and I have run five different bioinformatics tools that predict whether a particular mutation will lead to a more aggressive form of cancer or not (0-neutral;1-...
bandit_king28's user avatar
1 vote
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48 views

Should we train base learners on same folds when we stack different models?

I am confused about k-fold stacking. Should we train all the base learners on same folds? I mean is it ok to do the KFold split with different seeds or all base learners should trained on folds ...
CheeseBurger's user avatar
2 votes
1 answer
352 views

Model stacking, Super Learner Algorithm

I've recently started studying ensembles in ML, particularly Super Learner Algorithm. To be honest, although I have read several articles related to this topic, I am a little bit confused. I want to ...
User's user avatar
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8 votes
2 answers
811 views

Cross Validation in StackingClassifier Scikit-Learn

In Scikit-Learn StackingClassifier documentation it's written: Note that estimators_ are fitted on the full ...
malioboro's user avatar
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Stacking and Ensembling methods in Data Science

Recently it seems that stacking and ensembling methods have become more popular, and using these methods can give better results than using a single algorithm. My question is: What are the reasons, ...
Donald S's user avatar
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1 answer
190 views

Why stacking use strong learner as base learner?

I am wondering why stacking uses strong learners as base learners. How to understand it from expectation and variance way, or bias and variance way?
Bowen Peng's user avatar
1 vote
0 answers
33 views

Building a set of time-dependent ensemble models: Can I stack different models at each time step?

I am trying to predict the values of a physical quantity $A(t)$ at different time steps $t$ using Machine learning. The thing is that, because my training data are time dependent, I have to make a ...
pan's user avatar
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0 answers
67 views

Stacking using layer-1 models predictions on test set

I am new to Data Science and have been studying the methods of stacking to find out if it can meet the following fact, but I did not find or understand evidence that it can or cannot work. Let's ...
dg1996's user avatar
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1 vote
1 answer
429 views

Is this kind of stacked ensemble method prone to over-fitting?

I am working on a stacked ensemble method. I trained three classifiers as my first-layer models and one Logistic Regression as my second layer model. I then stacked both the first-layer models and ...
DataBach's user avatar
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4 votes
1 answer
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About the need of splitting data in stacking

I learned stacking of machine learning in a book, hands-on machine learning 2nd edition (2019). The picture was cited from hands-on machine learning 2nd edition (2019). In the above situation, ...
Crispy13's user avatar
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1 vote
0 answers
320 views

How to create a test set in stacking when doing cross validation

I am using Weka to implement stacking with k-fold cross validation. As I understand, we first divide our dataset in to k folds, then we use k-1 folds for training and 1 fold for testing. This ...
mary's user avatar
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1 vote
0 answers
31 views

Should initial features be added as input of meta-learner in stacking?

I'd like to know your opinion and reference on adding initial features (i.e. the ones used to train the weak learners) to the input of the meta learner (aside of the predictions from weak learners) ...
Alessandro Bitetto's user avatar
2 votes
0 answers
32 views

How to optimize hyperparameters in stacked model?

I was wondering whether somebody could explain how to optimize hyperparameters for the base learners and meta algorithm when stacking? In many tutorials they seem to be plucked out of thin air! ...
Jack's user avatar
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0 votes
2 answers
3k views

Why is my stacked model worse than my base models? [closed]

I'm learning stacking and start with the approach outlined in Introduction stacking I've plotted the data: I first would like to check if my algorithm is correct (see below): So I basically ...
Glork's user avatar
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1 answer
73 views

How valid is this Stacking Model (input features to weak learners are different)?

I have a set of features with 6 of them being categorical, 1 continuous and 2 textual in type. I have to predict the labels ( 10 in number) for them. I tried applying several models and came to a ...
Meenal Singh's user avatar