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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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, ...
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Combining two regression models using a meta-classifier

I know in stacking it is possible to build meta-regression models ontop of regression models to improve performance (meta-regression models for regression models) but are there any techniques if ...
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repeat time point linear regression in R

I have this data set and want to run mixed linear model. I am trying to study the effect of the binary variable "medicine" on the outcome (which is repeated measures of lab values at day 1, 2 and 3 - ...
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Stacking model: is finding best threshold necessary?

After performing a stacking model (with rstudio), is it necessary to choose the best threshold for it? In general after finding the best model among all the fitted models , you have to choose the ...
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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?
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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 ...
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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 ...
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113 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 ...
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What's the structural difference between “Stacked” and “Deep” systems?

How are the stacked learning algorithms different from deep ones e.g. Stacked SVM and Deep SVM?
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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, ...
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Ensemble learning: Efficiently stacking Neural Network model

I want to use ensemble learning for a classification task. I built three models. One of them is a neural network, which takes an hour to train. I want to use model stacking. I do not have the ...
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151 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 ...
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Can a classifier A get better result than classifier B when learning from the output of B?

I had the following problem recently: I tried to reverse engineer a classifier $C_1$. $C_1$ is an unknown, already in production classifier which I can't access. I can only access the result on past ...
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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) ...
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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! ...
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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 ...
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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 ...
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Are there specific Machine Learning Algorithms that are more indicated for Real Time Analytics?

As the title suggests, I am wondering if there are specific ML algorithms that are more suitable for real time learning. In my case, I am working on deploying a stacking algorithm on Spark Streaming ...
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I am learning to do stacking. I want to know what will the input be to level 2 classifiers [closed]

In classification/regression problems, say if we use five different base classifiers, we get 5 predictions for each example. What would be the input to the second level classifiers?
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Industry vs Kaggle challenges. Is collecting more observations and having access to more variables more important than fancy modelling?

I'd hope the title is self explanatory. In Kaggle, most winners use stacking with sometimes hundreds of base models, to squeeze a few extra % of MSE, accuracy... In general, in your experience, how ...
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Decrease the variance of ExtraTrees Classifier

I am trying to solve a machine learning problem. I am using ExtraTrees Classifier. When I am plotting the learning curves, I can see a wide gap. I need to decrease that (variance). I read about ...
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288 views

Why using Out-of-fold predictions as metafeatures in stacking?

So my question is essentially the same as this one: Why do we generate out-of-fold predictions for meta-ensembling/stacking? However, I am not entirely satisfied with the answer (not detailed enough ...
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Stacking without splitting data

I learned Stacking used in Ensemble learning. In Stacking, training data is split into two sets. The first set is used for training each model (layer-1, left figure), the second one is used for ...
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406 views

Generate ensemble of classifiers based on predefined feature subsets in R using mlr

I would like to create an ensemble classifier for a dataset and use different classification models for different subsets of features (these feature subsets are predefined as the data set I am working ...
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Why do we generate out-of-fold predictions for meta-ensembling/stacking?

Here's the guide I'm looking at: http://blog.kaggle.com/2016/12/27/a-kagglers-guide-to-model-stacking-in-practice/ Here's the relevant excerpt: The main point to take home is that we’re using the ...
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784 views

Combining bagging and stacking, with and without clusters and heteroskedasticity

Question 1: Start with the classing case of bagging, say in random forest. Fit $B$ trees to bootstrap samples of the data. Average the predictions of the $B$ trees to form a final prediction. ...
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153 views

Stacking or Voting - Multiple feature sets extracted with different parameters

I am extracting features from time series data using different parameters and then creating a SINGLE feature based data set with all features to perform classification. If I wanted to create separate ...
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955 views

Model stacking, what is the input of meta classifier?

I know that by stacking different models among which there has a low correlation can boost the performance of on single model. And I found a picture In step 7, the $h_j(x_i)$ in new data $x_i^{'}=\{...
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Stacking Lasso models

let say I have M subsets of independent variables and I want to use stack learner to predict dependent variable y. for each subset I use lasso method to get meta features (predictions). I have 2 ...
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Stacking, How can I check the correlation of models?

In ensemble learning, model stacking is a good way to improve the performance of a single model. However, models chosen to be stacked must have least correlation in order to exploit the performance of ...
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263 views

Can we stack the strong learners? [duplicate]

Stacking is done with combining all the weak learners. What will happen if we do it with strong learners? A case of overfitting?
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Models combinations

My goal is time series forecasting. I have created a number of models to make predictions. I know that forecast quality can be improved by combining predictions from different models(linear ...
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How to stack machine learning models in R

I am new to machine learning and R. I know that there is an R package called caretEnsemble, which could conveniently stack the models in R. However, this package looks has some problems when deals ...
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273 views

Using one ml models output to choose another models input

I'm dealing with a low event rate problem (e.g. credit card fraud). I've balanced my data with SMOTE, and ran a neural net model (cross validated with recall as the measure). However my precision (...
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Using different data transformation in ensembles

I have trained few models using sklearn and python. However I have scaled the data for Support Vector Machines - standarized and for Neural Network - Scaled [0-1] since it gives me better results and ...
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Why StackingRegressor doesn't catch the trend?

I just reviewed very good example of fitting StackingRegressor from mlxtend package. ...
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Model Stacking - Gives poor performance

I'm trying model stacking in a kaggle competition. However, what the competition is trying to do is irrelevant. I think my approach of doing model stacking is not correct. I have 4 different models: ...
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how do you stack models with 100% training accuracy?

Suppose I have several models, one of them $M$ has a 100% training accuracy. So regardless of how a stack the models, the stacked model is just M. e.g. If I use a linear model to stack them, then the ...
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576 views

Ensemble Decision Trees and Gradient Boosted Decision Trees

I see people often ensemble Gradient Boosted Decision Trees and Random Forests together. Does it make sense to ensemble a Decision Tree and a GBDT together? Isn't this DT already a part of GBDT?
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Stacking sensitivity analysis

I conducted stacking of three algorithms (NN, J48 and BN) with logistic regression as the meta-classifier. I am interested in doing a sensitivity analysis so I am able to rank the predictors and ...
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166 views

Training separate models for sets of features?

I'm wondering if it is sensible to train different models for different sets of features (I do not mean one model for each feature). Say I have 4 features - 2 are nominal while the other two are 2 ...
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645 views

Stacking Ensemble Meta Learning Components

I have been learning about Ensemble Algorithms. Reading this paper, I want to know about the meta classifiers that work well with Stacking Ensemble Learning Algo, since this work just talks about ...
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How do you calculate the covariance “stack up” of relative measurements?

Say that you have a measurement of $x_1$ relative to some globally fixed datum and a covariance for it, $\sigma_1$. If you have another measurement, $x_2$, taken relative to $x_1$ with its own ...
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177 views

Ensemble of boosting models

Are adaboost and gradient boost models highly correlated? Will including both the above models in ensemble improve accuracy significantly?? Because if the models are uncorrelated in the ensemble they ...
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1k views

How to properly do stacking/meta ensembling with cross validation

How do people use stacking or meta ensembling with cross validation in practice and in machine learning competitions like on Kaggle? Here are two approaches I've seen (but maybe neither is correct?) ...
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162 views

Does my classification model suffer from information leak?

I'm trying to solve a recommendation problem (recommending items to users). I have a dataset of triplets (user, item, reaction), where reaction is either 0 or 1 (...
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1k views

Stacking models which trained by different features in a data set for a classification problem

Normally, the first layer models in stacking and bledning method are trained by all features in a data set. However, what will be the performance of a big model based on first layer models trained by ...
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1k views

Stacking: Do more base classifiers always improve accuracy?

When using stacking, can accuracy always be improved by adding more base classifiers, types of base classifiers, and features?
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Stacking/Blending Predictive Models with More than Two Outcomes

I've been experimenting with stacking predictive models recently. I've mainly been focused on looking at making meta-models based off of predictive probabilities of smaller models while implementing ...
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Isn't stacking models a direct approach to overfitting?

With help by the discussions here I successfully trained various models for classification. As an example say I trained a stochastic gradient boosted model (gbm) and an extreme gradient boosted tree ...