Data Science Stack Exchange
Data Science Stack Exchange

Questions tagged [stacking]

18 questions
5
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1 answer

Can Boosting and Bagging be applied to heterogeneous algorithms?

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…
Ahmad Bilal
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2 answers

Stacking: Use predictions of train or test to create features for level 1 classifier

The question is pretty simple. In stacking, the predictions of level 0 models are being used as features to train a level 1 model. However, the predictions of what data? Intuitively it makes more sense to predict the test set and use those results…
liakoyras
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1 answer

How to use SMOTE in Stacking in SKLearn?

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…
Amin
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How does stacking help Bias and Variance?

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?
Mosleh Mahamud
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Stacking: How to best treat base learner?

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…
Peter
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Feature Importance in Stacked Model

I have built a stacked model using mlxtend StakingCVClassifier. I want to know the feature importance scores now. Is there any way I can calculate feature importance scores for the stacked model? If not can I combine the feature importance of…
Anjali
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1 answer

Feature Selection using Stacking Ensemble?

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…
Mimi
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1 answer

Feature importance difference in two similar machine learning models

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…
Manasvi Duggal
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What kind of algorithms can be used as a stacker in stacked generalization?

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…
Spider
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Stacking realization problems

I have two dataframes: x_train with features got from base models and y_train with ground true labels of these features using cross_validation. x_train f1 f2 f3 f4 f5 0 False False False True True 1 True False False False …
XEX
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1 answer

Understanding of stacking

I use various autoML solutions so I can't stack my models directly (for example via StackingClassifier, mlextend or as layers in keras) so I want to implement my own pipeline for this case using only models output files. The problem is that I have…
XEX
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Fit multiple models e.g classifiers -> stacking -> calibration without data-leak or getting too many datasets

I have some data X on which I want to do the following: Train two models; SVM and Logistic Regression Use a stacking classifier based on the models from (1) Calibrate the stacker from (2). We want to train a stacking-classifier on data of which…
CutePoison
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Stacking vs Blending. Why ever use blending?

If I understand correctly, stacking uses a set of "level 1" models, creates out of fold predictions and then trains these models on the full training data. The out of fold predictions are then used to train the meta learner. Blending uses a set of…
Koen
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KeyError: 'Resize' Error in converting from ONNX model to keras

I am trying to convert an ONNX model to a Keras model using onnx2keras, so that I can implement this: (https://machinelearningmastery.com/stacking-ensemble-for-deep-learning-neural-networks/) stacking model on Yolov5. It will convert the majority of…
Thomas O'Brien
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Accuracy is decreased, and loss is not changing for logistic regression of stacking model meta learner

Problem: I would like to improve accuracy of stock price prediction image classification model using candlestick charts. Base model: VGG16 and EfficientNet. Base model input: Two models independently take labeled candlestick chart images as…
DreamtoSiliconValley
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