Questions tagged [ensemble]

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5 votes
1 answer
258 views

Methods for ensembling ranked lists?

I was wondering if there's a good way to use ensembling when I have two or more algoritims producing ranked lists. That is, suppose I have the following datasets consisting of ordered lists (higher ...
0 votes
1 answer
79 views

Ensemble method with Blackbox Classifiers

I have few pre-trained blackbox models that are used for classification tasks. I want to know what is the best way to combine these models into a single classifier that does not require any re-...
3 votes
1 answer
151 views

Physical modelling with neural networks - single output + stack ensemble vs multi-output

We are trying to replace an existing physical model (8 inputs/7 outputs) with artificial neural networks. The physics behind the existing model is mainly thermodynamics of humid air for air ...
6 votes
2 answers
203 views

Gridsearch XGBoost for ensemble. Do I include first-level prediction matrix of base learners in train set?

I'm not quite sure how I should go about tuning xgboost before I use it as a meta-learner in ensemble learning. Should I include the prediction matrix (ie. df containing columns of prediction results ...
3 votes
2 answers
1k views

grid search - optimal weighting of classifiers

I am using three different of the shelf classifiers. It's a three class classification task. I want to calculate the optimal weights (c1weight, c2weight, c3weight) for each classifier (real task more ...
0 votes
0 answers
21 views

Can you please provide one fully solved gradient boosting regression numerical example (not python code) [duplicate]

Can you please provide one fully solved gradient boost regression numerical example (not Python code).
0 votes
0 answers
8 views

Record / subject wise aggregate classification

In the project I am studying I have a set of records (each record is a 1D signal) with variable length. For normalizing length and data augmentation, I am segmenting them in fixed length samples so I ...
3 votes
3 answers
2k views

Combining Classifiers with different Precision and Recall values

Suppose I have two binary classifiers, A and B. Both are trained on the same set of data, and produce predictions on a different (but same for both classifiers) set of data. The precision for A is ...
1 vote
0 answers
53 views

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 ...
0 votes
0 answers
98 views

Voting classifier ensemble error: 'ValueError: Unknown metric function: 'function'.'

I am trying to create a hard voting ensemble of three neural networks. I've already converted them to Keras Classifiers. Here is the code: ...
0 votes
3 answers
11k views

"ValueError: Data cardinality is ambiguous" in model ensemble with 2 different inputs

I am trying a simple model ensemble with 2 different input datasets and 1 output. I want to get predictions for one dataset and hope model will extract some useful features from the second one. I get ...
1 vote
0 answers
32 views

Is ensemble model black box?

Does all ensemble model variants such as voting, bagging and stacked considered black-box model? Since it is difficult to visualize and interpret the model especially when complexity increases?
6 votes
3 answers
836 views

Is it possible to build ensemble models without a decision tree?

Is it possible to build ensemble models without a decision tree? I know that the description of ensembles itself suggests otherwise. However, I am really new to machine learning and all the ensemble ...
1 vote
1 answer
173 views

How to assign a weight for classifiers when using weighted majority voting?

I am trying to apply weighted majority voting on an ensemble as a combiner method. I read different papers and articles, however, I am still a bit lost on: How the weighted majority voting works How ...
0 votes
1 answer
779 views

What are some good models to complement XGBOOST in stacking?

What are some good models to complement XGBOOST via stacking in typical Kaggle datascience competition? I realize XGBoost with well-tuned hyperparamters are generally quite good already.
2 votes
1 answer
107 views

How do I combine predictions from classifiers for two different problem?

I am working on a classification problem for predicting whether the shipment is going to be late or not. I would say the classifier is mediocre at predicting the positive class at the moment. But the ...
0 votes
0 answers
27 views

How to write decider function for multiple models

I have trained two classifiers .. Text Classification and Image Classification. So both models gives score for each class. For example there are 3 classes. Each model give array of 3 confidence score ...
2 votes
2 answers
75 views

Can i use other regression types that arent based in decision trees to use it like a weak learners in gradient boosting?

I was thinking if i can use polynomial regression like a weak learners in gradient boosting but i read that decision trees are used for that and i cannot find anything that show me the possibility of ...
0 votes
1 answer
29 views

Individual models gives quite same distribution on Test set, whereas Ensembling gives better result but very different distribution

I am working on a binary classification problem with unbalanced data (17% for positive class). The problem is as following: My three individual models when predicting on the test set (for which I don'...
1 vote
0 answers
175 views

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?
3 votes
1 answer
460 views

Bagging vs pasting in ensemble learning

This is a citation from "Hands-on machine learning with Scikit-Learn, Keras and TensorFlow" by Aurelien Geron: "Bootstrapping introduces a bit more diversity in the subsets that each predictor is ...
1 vote
3 answers
168 views

Ensembling expressions

I have two models, $m_1$ and $m_2$, and I want to ensemble them into a final model. I want to be able weight one or the other more according to a grid search. There are two main ideas that come to my ...
0 votes
1 answer
606 views

How to train with cross validation? and which f1 score to choose?

I got similar results in 2 models which consists of similar algorithms. Model 1 with cv=10 has a f1'micro' of 0.941. See code below. Model 2 only train test split (no cv) has f1'micro' 0.953. Now here ...
0 votes
0 answers
206 views

What is the best way to use Early Stopping in an ensemble (stacking) model?

I have a training and a test dataset. I would like to use the output of Model A in an ensemble model. However, I would like to use early stopping. Usually, I would create Model A for each K-fold (on ...
4 votes
3 answers
314 views

How to create an ensemble that gives precedence to a specific classifier

Suppose that in a binary classification task, I have separate classifiers A, B, and C. If I ...
1 vote
0 answers
24 views

Domain specific language to describe ensemble model

I'm looking for some tool/library/widely used approach to describe Hierarchical Model structure like ensemble: It's absolutely straightforward how to do it with simple ensemble like It can be ...
4 votes
1 answer
133 views

Visualizing F-score differences in information extraction

I have several corpora and NLP systems (including a few merge ensembles of output of these systems combined in unions and intersections) with which I have extracted the annotation span sets {(begin, ...
1 vote
1 answer
481 views

Handling Categorical Features on NGBoost

Recently I have been doing some research on NGBoost, but I could not see any parameter for categorical features. Is there any parameter that I missed? ...
1 vote
1 answer
34 views

What should I use as training data for base (level 1) classifiers in ensembling?

Can I just take all training data that I have, train the base models on them and then take their results and use them for training level 2 model? Is this a good practice, or should it be done ...
2 votes
1 answer
1k views

What is the difference between horizontal and vertical ensemble?

I am looking at different ways to do model ensembling and I came across the terms horizontal and vertical blending/ensembling but it is not well defined. My questions will be: What is the ...
0 votes
1 answer
33 views

How can the Adaboost technique be called an ensemble learning technique?

I have read that in ensemble learning we use the outputs of various classifiers to make the predictive modeling better but in Adaboost we just use one classifier and we make it a strong learner but ...
2 votes
1 answer
151 views

When and how to use bagging?

Can all types of ML methods benefit from bagging? Decision Tree Classification seems always be the go-to example of bagging, what about other classifiers or regressions? When it's suitable to do ...
1 vote
1 answer
48 views

How can I make ROC and compute AUC?

I created a boosting tree and got the probability for each tuple in my testing set. But I'm confused on how to combine each probability. Can someone tell me how to combine the probabilities?
1 vote
2 answers
690 views

How to visualize Ensemble Models ( Random Forest) with 1000 estimators

I am working on classification problem where I need to categorize the user in buy/ non-buy category. I have around 100 + features or predictors to predict the behavior of user. I tried to implement ...
1 vote
0 answers
467 views

How to create an ensemble in tensorflow using tf.estimator?

I have created a neural network using tensorflow's estimator API: ...
4 votes
1 answer
1k views

Geometric and harmonic means in ensembling methods

When using ensembling methods for regression, a common approach is to average (using the arithmetic mean) the outputs of the weak learners in order to obtain the output of the ensemble. Is there a ...
2 votes
2 answers
582 views

xgboost cannot identify perfectly fitting regression line

For a dataset I want to use xgboost for the optimal ensembling of $n$ forecasts instead of just using their arithmetic mean for combination. I found that xgboost generates forecasts that are worse ...
1 vote
1 answer
93 views

Can clustering my data first help me learn better classifiers?

I was thinking about this lately. Let's say that we have a very complex space, which makes it hard to learn a classifier that can efficiently split it. But what if this very complex space is actually ...