Questions tagged [ensemble-modeling]

In machine learning, ensemble methods combine multiple algorithms to make a prediction. Bagging, boosting, and stacking, are some examples.

Filter by
Sorted by
Tagged with
0 votes
1 answer
15 views

Combining results from classifiers trained on different test/train splits results in higher accuracy

I have developed a classifier model using LightGBM. The accuracy of the model varies significantly because of the test_train_split state(between 83% and 91%). This ...
1 vote
1 answer
26 views

Predicting many classes, is it a known solution to build n-group classifiers?

Imagine you want to predict 2048 classes. Instead of asking one model to predict all of them at once, is it a known type of solution to have a model predict which cluster or group of classes an input ...
0 votes
0 answers
29 views

What is the formula to combine N correlated classifiers into single optimal one?

As we know if we train N probabilistic classifiers on same dataset, they will have some degree of correlation. As we also know, there is some method to assign optimal coefficients/factors/weights to ...
1 vote
1 answer
16 views

Combining multiple ranked lists

Suppose I'm given two ranked lists, A and B, with each item in the lists being associated with a score: ...
0 votes
0 answers
30 views

In ensembles combining models, does it make sense for a model to have negative weight?

I have 13 models ranging from simple models like Seasonal Naïve Average to complex models like Random Forests, The weights of the models is calculated based on the LPMinimize of the error during the ...
2 votes
2 answers
32 views

Difference in proportion between labelled sample and the population

I'm working on a project to predict bots from legit users from social medias. The data that I collected has about 5% of bots for 95% of legit users. The problem is as I labelled my data, I was more ...
  • 222
0 votes
0 answers
15 views

Stacked ensemble model

In regression, multicollinearity between variables would need to be removed to suit the model assumption. In building a stacked ensemble model, with say SVM, xgb and a decision tree as a base model ...
0 votes
0 answers
7 views

Serialized models into Voting Classifiers. Is it possible?

It's a simple question actually. I've created 3 models. RandomForestClassifier, GradientBoostingClassifier, and XGBClassifier. Then I dumped all of them with joblib. I would like to know if is it ...
0 votes
1 answer
19 views

Help me name my problem – Online Ensemble Realibration

I have the following problem: k predictors (let's say A, B) . Each predicts a value and ...
  • 183
1 vote
1 answer
12 views

Stacked ensemble model characteristics

In which areas/ problems stacked ensemble is useful compared to other models in a specific industry/ application. Commonly, either simple model such as linear regression is utilized for explanaibility ...
0 votes
0 answers
21 views

Ensemble model for a recommender system

Suppose I have three models - A, B, and C - all of which are good candidates for a Recommender system. I want to build an ensemble, combining the three models, but I'm not sure how to proceed. Suppose ...
0 votes
0 answers
12 views

Would Stacking improve accruacy if base model accruacy is not good?

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 ...
0 votes
1 answer
33 views

two different model into a single model

Lets say that I have a model that detects Apples, oranges and grapes in an image and I also have another model that detects Jack fruit and Banana in a image. So how do I create a model such that the ...
0 votes
1 answer
16 views

Discussion: should we feed the original features as inputs to an ensemble model?

Let's imagine you have different models to give predictions on the same topic. One of your model is a regression, the other an ANN, the last one XGBOOST. Some of your models work better predicting at ...
0 votes
0 answers
12 views

If a machine learning model can be trained to obtain B from A, and another to obtain C from B, could a final model obtain A from C?

I've recently been working on a regression model based on some physics to obtain some numbers C from a set of features A, although with little success. Knowing that the formula that relates A to C ...
2 votes
2 answers
61 views

Finding the best data source query strategy for a ml model. Maximizing quality, minimizing cost

It is too expensive to query all data sources for each claim, so it is necessary to define a sourcing strategy to maximize the model quality score and minimize the cost of prediction (expressed in ...
  • 366
0 votes
1 answer
25 views

Training XGBoost on time series features of varying sample length

I have some time series data that contain features that that go back anywhere from 5 to 50 years. I've considered imputation (e.g. taking the mean), but I'm not sure it's feasible to impute such large ...
0 votes
0 answers
26 views

Combining DeepXDE (physics-informed neural networks) with other tensorflow models

I would like to stack two models from scikit-learn and tensorflow. I have tried to make an illustration of what I want to do. What I am looking for is the actual wrapper. Does scikit-learn have any ...
0 votes
1 answer
18 views

Where should I stop training if I want to bag models

Let's say I have a clear case of overfitting where my loss curves look like this (x axis are iterations): Now I would like to try bagging to reduce the variance, where should I stop models training? ...
  • 111
0 votes
1 answer
645 views

Found input variables with inconsistent numbers of samples: ValueError

Today I am trying build ensemble model. Where I am working with iris dataset. In my model I am using ...
0 votes
1 answer
23 views

Understanding Classification model results

In a certain binary classifcation problem I am getting a AUC of 1 and Accuracy,FI,Recall,Precision of ~99.7 both in train,test and holdout sets. But when I run the model on unlabelled data which I ...
  • 101
0 votes
0 answers
32 views

What is a good way to model this?

I have a dataframe df which looks like this : user Date TP_A TP_C TP_D TP_E TP_B TP_F Order 1 11-07-2014 0 0 1 0 0 0 0 1 11-07-2014 0 0 0 1 0 0 0 1 15-07-2014 0 ...
  • 101
0 votes
0 answers
35 views

Why would an ensemble model perform worse than all individual models? biomod2

I'm using biomod2 in R and my ensemble model performs worse on the evaluation data (drastically lower ROC, 0.835) than any of the individual models (ROC ranges 0.89-0.97). What could be causing this? ...
0 votes
2 answers
41 views

If you are making a ensemble model does training data on base models have to be different from one another

I was reading this article talking about ensemble models. I was interested in the max voting model using 3 base learners. However, I am a little confused about the process. Currently, I'm thinking it ...
  • 13
0 votes
0 answers
10 views

Univariate Time Series Revenue Forecast Flow for Multiple Products (Different Products in Same Domain)

My task is revenue forecast. I would like predict 7 days horizon for each 10 products. I am planning to use ensemble model. Can 10 products(same domain, for ex: ios game app revenues) be predicted ...
  • 101
2 votes
2 answers
153 views

Are feature importances of ensemble methods sensible interpretable?

As mentioned in the question, it is easy to interpret the meaning of features in algorithms like simple decision trees. But in the case of ensemble methods that are known to average/modify features, ...
  • 23
0 votes
1 answer
31 views

Voting Regression models, other approaches than averaging the results from each estimators

In a regression problem that I'm currently working on, it seems that my model is doing well on higher values but significantly worse on lower values (e.g. values from 100,000,000 to 105,000,000 are ...
  • 103
0 votes
0 answers
43 views

Can I use different dataset when performing model stacking?

Let's say I want to detect new species of fish. I have several models, each trained to recognize a different characteristic, e.g., the speed of known fish, the size of known fish, their known shapes, ...
  • 121
1 vote
1 answer
16 views

Outputs from models (trained on different data) as inputs to another model?

Let's say I want to detect new species of fish. I have several models, each trained to recognize a different characteristic, e.g., the speed of known fish, the size of known fish, their known shapes, ...
  • 121
1 vote
1 answer
148 views

What is meant by this notation for ensemble classifier error rate

The below is a picture which denotes the error of an ensemble classifier. Can someone help me understand the notation What does it mean to have (25 and i) in brackets and what is ε^1 is it error of ...
  • 125
0 votes
1 answer
614 views

How to make an ensemble model for classification with pytorch using trained models?

I am trying to make an ensemble model composed of two pre-trained models, using torch, in order to classify an image. Below is some code, based on this post. ...
  • 123
1 vote
1 answer
143 views

Random LightGBM Forest

I'm not completly sure about the bias/variance of boosted decision trees (LightGBM especially), thus I wonder if we generally would expect a performance boost by creating an ensemble of multiple ...
1 vote
2 answers
1k views

Output of a model as additional input of another model to solve the same task

I was wondering about whether it is possible to train a ML model for a classification task with dataset D, and then train another model to solve the same classification task, which takes as input ...
0 votes
0 answers
15 views

Combining features from multiple models and optimising features

I have multiple models predicting an outcome (continuous) and I want to take action to optimize the best values of these features to make a decision. Consider a regression model, y1 = m1x1 + m2x2 + ...
1 vote
1 answer
31 views

Is ensemble learning a subset of meta learning?

I'm studying ensemble learning methods, focusing on random forest and gradient boost. I read this article about this topic and this about meta-learning. It is possible to say that ensemble learning is ...
  • 471
0 votes
1 answer
538 views

Is there a way to combine multiple ML models where each use datasets with different features?

I have a dataset where some features (c,d) apply to only when a feature (a) is a specific value. For example ...
  • 1
1 vote
1 answer
22 views

How should I handle time-duration-based columns in classification?

For example, say I am trying to predict whether I will win my next pickleball game. Some features I have are the number of hits, how much water I’ve drinken, etc, and the duration of the match. I’m ...
0 votes
1 answer
37 views

Stacking neural nets with cross validation

I am trying to implement stacking model for a ML problem and having hard time figuring out the cross validation strategy. So far I have used 10-fold cross validation for all my models and would like ...
0 votes
0 answers
30 views

Combine Content based model and collaborative filtering model

I'm building a ML model for personalization page. I have two models currently one is content based and another is collaborative filtering. Can someone tell me how can I combine both models and use ...
  • 183
0 votes
0 answers
31 views

Ensemble Model for Recommendation Engine

I want to build an ensemble recommendation engine where I can combine Surprise library algorithms like KNN and SVD to achieve the best result. Can anyone know how to ensemble this technique?
0 votes
0 answers
94 views

How to suppress Python from showing status of my random forest run?

How can I stop scikit-learn Random Forest from displaying the following status output during run? I have already set verbosity to -1 in RandomForestClassifier() and GridSearchCV() ...
0 votes
1 answer
714 views

How does XGBoost perform in Parallel

So what I know about boosting technique, Like we train the data and update the weights of falsely predicted values or try to minimize the loss in the next model. So basically, it's the sequential ...
  • 183
6 votes
3 answers
627 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 ...
0 votes
1 answer
19 views

Additional business rules in ensemble methods (RF, Boosted Trees)

How is it possible (if at all) to implement additional business constraints to an ensemble machine learning model, such as random forests or boosted trees? These additional business rules can be ...
  • 165
2 votes
1 answer
146 views

Using output of ML algorithms as an input to different ML algorithm (Ensemble Learning)

I want to assign weights to multiple models and make an single ensemble model. I want to use my outputs as the input to a new machine learning algorithm and the algorithm will learn the correct ...
1 vote
1 answer
45 views

Boosting algorithms only built with decision trees? why?

My understanding of boosting is just training models sequentially and learning from its previous mistakes. Can boosting algorithms be built with bunch of logistic regression? or logistic regression + ...
  • 385
2 votes
1 answer
45 views

When should you use Ensemble?

I have been working on a project as a part of my studies(computer/data science). I tried to make the best classifier I can with what I learned, and recently I have tried to upgrade this classifier ...
0 votes
2 answers
45 views

How can compare suggestion models with different performances?

I have 4 class binary classification models. That models identify which class a particular students is suitable for. For example, we have user 1 and 4 classes ...
  • 101
2 votes
1 answer
99 views

Making an ensemble model for high F1 score

I presently have 2 algorithms that have a numerical output. Using a threshold of 0.9, I get the classification output. Let's say they are: P (high precision, low recall) R (high recall, low precision)...
1 vote
1 answer
81 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 ...
  • 53