Questions tagged [ensemble-learning]
For questions related to ensemble learning, which refers to machine learning techniques where multiple models (e.g. a neural network and a decision tree) are trained and their predictions are combined to solve the same problem. Bagging and boosting are two popular ensemble learning techniques.
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Does it make sense to combine classifiers trained on the same dataset?
I am working on a classification problem.
I have a dataset $S$ and I am training several prediction algorithms using S: Naive Bayes, SVM, classification trees.
Intuitively, I was planning to combine ...
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How to make an LSTM ensemble model with different input shapes
This is what I got so far for making an lstm ensemble with one model input for each of the lstm models and for the ensemble model and it works perfectly.
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How to make an ensemble model of two LSTM models with different window sizes i.e. different data shapes
Below is the Python code for making an ensemble model. All the inputs are the same for all three models. But what if the models have different input shapes due to different window size, such as LSTM ...
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When do two identical neural networks have uncorrelated errors?
In Chapter 9, section 9.1.6, Raul Rojas describes how committees of networks can reduce the prediction error by training N identical neural networks and averaging the results.
If $f_i$ are the ...
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37 views
What ensemble methods are used in the state-of-the-art models?
What ensemble methods are used in the state-of-the-art models?
When I surveyed the state-of-the-art methods of classification and detection, e.g. ImageNet, COCO, etc., I noticed that are few or even ...
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When do the ensemble methods beat Neural Networks?
In many applications and domains : Computer Vision, Natural Language Processsing, Image Segmentation, and many other tasks - neural networks of a certain architecture are considered to be by far the ...
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23 views
Would performance of atomic models matter in ensemble methods?
Suppose I have two fitted ensemble models $F_1 := (f_1, f_2, f_3, \cdots f_n)$ and $G_1 := (g_1, g_2, g_3, \cdots g_n)$.
And they were using the same ensemble methods (boosting or bagging).
And I am ...
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0answers
23 views
Why don't ensembling, bagging and boosting help to improve accuracy of Naive bayes classifier?
You might think to apply some classifier combination techniques like ensembling, bagging and boosting but these methods would not help. Actually, “ensembling, boosting, bagging” won’t help since their ...
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1answer
41 views
How is an architecture composed of a second model that validates the first one called in machine learning?
I have a mix of two deep models, as follows:
if model A is YES --pass to B--> if model B is YES--> result = YES
if model A is NO ---> result = NO
So ...
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1answer
33 views
Why does the error ensemble use the ceiling of the number of classifiers?
What is $y$? Why is $k$ the ceil of $n/2$? What is $y \geq k$?
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How can we combine different deep learning models?
I know that ensembles can be made by combining sklearn models with a VotingClassifier, but is it possible to combine different deep learning models? Will I have to make something similar to Voting ...
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2answers
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Are there ensemble methods for regression?
I have heard of ensemble methods, such as XGBoost, for binary or categorical machine learning models. However, does this exist for regression? If so, how are the weights for each model in the process ...
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1answer
109 views
How do I check that the combination of these models is good?
I've selected more than 10 discriminative (classification) models, each wrapped with a BaggingClassifier object, optimized with a ...
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2answers
1k views
Do deep learning algorithms represent ensemble-based methods?
Shortly about deep learning (for reference):
Deep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using a deep graph ...
