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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Do deep learning algorithms represent ensemble-based methods?

According to the Wikipedia article on deep learning: 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 ...
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4 votes
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75 views

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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3 votes
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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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3 votes
0 answers
59 views

When do the ensemble methods beat neural networks?

In many applications and domains, computer vision, natural language processing, image segmentation, and many other tasks, neural networks (with a certain architecture) are considered to be by far the ...
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2 votes
1 answer
44 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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2 votes
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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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  • 286
1 vote
2 answers
146 views

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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1 vote
1 answer
115 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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  • 177
1 vote
1 answer
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How do I take the correct classification predictions of an ml algo (i.e. random forest/neural net) and sort the instances in each category?

I am trying to sort the instances within each of 5 classification categories in a dataset that has been put through both a random forest classifier and a neural network with 99% accuracy on each. ...
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0 answers
57 views

How to calculate uncertainty in Deep Ensembles for Reinforcement Learning?

Lets take the following example: I must predict the return (Q-values) of x state-action pairs using an ensemble of m models. Using NumPy I could have the following for x = 5 and m = 3: ...
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  • 81
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1 answer
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In ensemble learning, does accuracy increase depending on the number of models you want to combine?

I want to predict using the same model as multivariate time series data in a time series prediction problem. Example: ...
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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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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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0 votes
1 answer
49 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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0 votes
1 answer
58 views

Is there an approach where the output of one neural network is used to choose the next neural network?

I'd like to design a deep learning architecture in which the output of a primary neural network $M_{\theta}$ determines which neural network $N^i_{\alpha}$ in a set of secondary networks $\mathcal{N}$ ...
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How to properly combine multiple readings/measurements?

In an AI application (for example, self-driving), there are usually many different reading devices/sensors to ensure the outcome is correct. More specifically, a self-driving car can use object ...
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Build the architecture of an ensemble model for tabular classification

I have data that looks like this: ...
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  • 111
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1 answer
54 views

Multiclass Ensemble Methods with weak classifiers under 50%

Normally, when using an ensemble method, such as baggin or boosting, in binary classification, there is a reqauirment that each weak classifier have accuracy better than 50%. In the multiclass ...
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How to improve the PMI (Pointwise Mutual Information) Quality for document based PMI

Generating word embeddings from the PMI is well understood and known to be equivalent to SGNS (skipgram negative-sampling) under certain conditions. I was able to get good quality word embedding using ...
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  • 101
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30 views

How to ensemble two different computer vision models?

I have prepared two distinct models: Representing contour of the image Representing edges of the image. I would like to create a model which can take advantage of both models in predicting data. May ...
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0 votes
0 answers
250 views

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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1 answer
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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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