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Questions tagged [ensemble-modeling]

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

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How can I combine/pool of the results of regression with neural network?

My study has ten imputed dependent variables (plausible values). After separately analyzing each dependent variable using a regression neural network (NN), I must combine/pool the results. I tried ...
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Uncertainty in stacked ensemble model

I am using the stacked generalization scheme to combine the predictions from different machine learning models (input models from now on). I am currently calculating the prediction interval for each ...
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Select estimators for Voting classifier

I don't know the efficient method to select the best subset of the estimator that saves time and highest accuracy. How to choose the best estimators for the Voting classifier? Voting Classifier is an ...
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How to use two independent datasets in machine learning phd research work?

In order to develop an academic performance prediction model for a local Higher Ed Institution, I have collected the OULAD open dataset and the local Institution's dataset which I structured into the ...
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Is a Random Forest Capable of Learning and Predicting Numerical Trends in Panel Data?

In a panel data set consisting of exponential functions, each indexed by an integer i ranging from 0 to 100. The exponential function is defined as f(i, t) = A(i) * e^(-r(i) * t), where A(i) is the ...
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Need Expert Advice for using sklearn pipeline to create a composite estimator with multiple models & features. Column Transformer with Mixed Types

I am trying to use sklearn pipeline to create a composite estimator. Please check attached image for model blueprint. Can anyone help me understand how this can be done in python. Column Transformer ...
prafull ghare's user avatar
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how to build model using two input dataset in which there is no common column to merge or combine

I want to create model for truck company in which trucks delivers the car for customers.i have two data sets. one is customer details like how many cars they want from particular area or terminal and ...
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Using auxiliary softmaxes to measure impact of each submodule on the final softmax classifier

I am attempting to assess the impact of various submodules (CNN 1D, CNN 2D, CNN 3D, FFNN) on the final classifier of the neural network that i am currently building. The neural network itself is ...
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Adding new model to ensemble without fitting again

Currently i have the following code ...
Guilherme Raibolt's user avatar
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Regression models that conform to functional groupings of features

For example, suppose we want to predict y with features x1, x2, x3, x4. If I specify ...
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How can I improve accuracy of my ensemble (or anywhere in the code where I can increase accuracy)?

I am pretty new to machine learning, so if my code is not good, please bear with me. ...
MrPizza FarmerDude's user avatar
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What data should be used to train an ensemble of pre-trained models?

I split my dataset into train and test data. Then I trained logistic regression, SVM, and random forest models using pipelines with cross-validation and train data. I saved best-performing models with ...
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How to reduce the false positives to improve the models performance?

I am currently building a binary classification model to predict order return rates. I used the GradientBoostingClassifier for training the model and also performed hyperparameter tuning using ...
Kedharnath Kb's user avatar
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Need help with improving validation loss and model overfitting/underfitting

I am using Ensemble PyTorch to train a voting classifier. My dataset includes around 60k records. I trained a Neural Network with Cross-entropy loss. Below is my model architecture ...
Abhinav's user avatar
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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 ...
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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 ...
Jonathan Allen Grant's user avatar
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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: ...
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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 ...
Justice_Lords's user avatar
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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 ...
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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 ...
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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 ...
Kusisi Karem's user avatar
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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 ...
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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 ...
Vincent H's user avatar
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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 ...
Andrew's user avatar
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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 ...
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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? ...
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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 ...
Fozle Rabbi's user avatar
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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 ...
Scope's user avatar
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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 ...
Scope's user avatar
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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 ...
ddd's user avatar
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2 answers
235 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, ...
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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 ...
strivn's user avatar
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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, ...
pookie's user avatar
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1 answer
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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 ...
Arun Jose's user avatar
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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. ...
dfrankow's user avatar
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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 ...
CutePoison's user avatar
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2 answers
5k 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 ...
DanielRoncel's user avatar
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1 answer
139 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 ...
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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 ...
Bruce's user avatar
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1 answer
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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 ...
NFeruch - FreePalestine's user avatar
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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 ...
LoneWolf's user avatar
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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() ...
lostwanderer's user avatar
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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 ...
Chris_007's user avatar
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6 votes
3 answers
869 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 ...
Raveen Diaz's user avatar
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1 answer
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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 ...
Jivan's user avatar
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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 ...
Parth Sharma's user avatar
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1 answer
60 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 + ...
haneulkim's user avatar
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1 answer
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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 ...
L. breitman's user avatar
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51 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 ...
Sogo's user avatar
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1 answer
187 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)...
Kanishk Mair's user avatar