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 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() ...
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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 ...
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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 ...
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11 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 ...
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How can I use Ensemble learning of two models with different features as an input?

I have a fake news detection problem and it predicts the binary labels "1"&"0" by vectorizing the 'tweet' column, I use three different models for detection but I want to use ...
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Ensemble/combining models weighted by number of observations?

Across a few different projects, I have hit a problem where I have two (or more) models: General-Purpose Model: A model which is based on a large amount of data not specifically relevant to my ...
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Stacking Classifier Error

I am using the Stacking classifier and getting following Error CODE ...
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1answer
46 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 ...
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36 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 + ...
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26 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 ...
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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 ...
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1answer
29 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)...
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32 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 ...
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203 views

Probability that ensemble model is correct based on accuracies of its classifiers

I'm trying to understand what I did wrong when trying to answer this question. The exact question is: Assume that we have 3 trained prediction models, and each model outputs either -1 or 1. We then ...
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Understanding Weighted learning in Ensemble Classifiers

I'm currently studying Boosting techniques in Machine Learning and I happened to understand that in Algorithms like Adaboost, each of the training samples is given a weight depending on whether it was ...
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How to make an lstm ensemble with different input shapes using keras

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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Large general-purpose model vs ensemble of many smaller models

I am reading this paper - https://arxiv.org/abs/1503.02531v1 - devoted to knowledge distillation in neural networks. One interesting approach is mentioned in this paper in sections ...
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28 views

Base model in ensemble learning

I've been doing some research on ensemble learning and read that for base models, model with high variance are often recommended (can't remember which book I read this from exactly). But, it seems ...
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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?
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Different types of Ensemble learning methods [closed]

I've been reading and searching information about different types of Ensemble learning methods however I am a bit confused and want to make sure my understanding is correct. Below is graph of how I ...
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precision recall curve is like stairs [closed]

I am training an ensemble model using a 400 data set sample this led to a precision recall curve that looks like stairs ? what would be the reason beside the low number of samples ?
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Hybrid Ensembling

I read a paper recently where researchers were trying to do classification using ensembling methods. I first read about the concept of BOOSTED-STACKING there.For those who dont know , I can give a ...
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How to interpret gradient descent in boosting ensembles?

I struggle to grasp the role of gradient based optimization in boosting ensembles. As far as I understand boosting means combining a bunch of estimators (of the same types, usually decision trees) ...
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1answer
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How many run should we implement on the machine learning model?

Theoretically, we can implement fix seed on the machine learning model to get the same results every run (reproducible)but it may leads to bias. So, in order to prevent bias, I gonna run the model ...
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How do I proceed with model selection?

Only data scientist in an organization and I could really use a sounding board here. In Phase One of a project I deployed four models and served their average as the prediction. I used Random Forest ...
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163 views

How to tune an weighted voting ensemble method?

I am working on kidney cancer patients' data with 5 unbalanced labels. These codes are contained of Normalization, Oversampling on Feature Engineering part. A list of 9 ordinary Machine Learning ...
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126 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 ...
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38 views

What if the votes for 2 classes are equal in an ensemble learning technique?

Suppose in ensemble learning technique, if the number of models that predict class 1 is equal to the number of models that predict class 0. Then, which class will be decided as output?
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243 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 ...
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DecisionTreeRegressor under the hood of GradientBoostingClassifier

I'm inspecting the weak estimators of my GradientBoostingClassifier model. This model was fit on a binary class dataset. I noticed that all the weak estimators under this ensemble classifier are ...
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45 views

How to improve model performace when model shows a systemic pattern in residues

I'm working on a regression model using Boosting algorithms (CatBoost, XGBoost, and LightGBM). All models give similar accuracy of 0.2 RMSE (Target varies from 0 to 1). I obtained the following plots ...
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191 views

In XGBoost, how is a leaf index corresponding to the particular leaf node in actual base learner trees?

I've trained a XGBoost model for regression, where the max depth is 2. ...
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1answer
310 views

Are "Gradient Boosting Machines (GBM)" and GBDT exactly the same thing?

In the category of Gradient Boosting, I find some terms confusing. I'm aware that XGBoost includes some optimization in comparison to conventional Gradient Boosting. But are Gradient Boosting ...
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93 views

Stacking - Appropriate base and meta models

When implementing stacking for model building and prediction (For example using sklearn's StackingRegressor function) what is the appropriate choice of models for the base models and final meta model?...
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34 views

How Adaboost calculates error for each weak learner in training?

I am studying the Adaboost classification algorithm because i would like to implement it from scratch. I understand how it works, but i am not able to understand where some steps are placed. I will ...
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85 views

How to compare Random Forest with other models

I am new to Machine Learning and I am trying to undrestand the Out of Bag Error in Random Forests and its use. Let's say that we have a dataset. First we use the whole dataset (without splitting it) ...
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19 views

Selecting models for ensemble from large group of models with high uncertaintly

I'm in a situation where many models have been created, and I have their cross-validation performances as well as performance on test data. I need to select models for inclusion in a simple bagging ...
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15 views

General practices for building an incremental learning model which never forgets?

I'm new to datascience and appreciate your sage advice! I need to build an incremental learning model, and I know there's a lot that goes into something like that, but I'd like to highlight the most ...
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1answer
27 views

Model with 2 datasets: combine time series data and statistics

I am new to data science modelling so apologies if using wrong terminology in advance. I have a standard time series dataset of historical prices which is used to train/test a simple Random Forest ...
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70 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 ...
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1answer
35 views

Augmenting the validation set in Ensemble Model

I have 8 models which I have trained on 90% of my set (training set) and tracked its performance on the loss of the validation set (10% of the original set). I want to generate an ensemble model by ...
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548 views

Is there any way to plot ROC curve for Ensemble hard voting classifier?

I am working on a multi-class text classification problem and performing an Ensemble learning for text classification. I chose hard voting as ensemble technique. I tried to plot ROC curve for my ...
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Decision Boundary with random new observations vs observations from test set

I'm trying to plot decision boundary for Decision Tree classifier. Classifier is trained on training set, and decision boundary (contour) using random new observations and observations from test set ...
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48 views

Classification model accuracy with ensemble methods

I came through this statement in a Machine Learning text book based on law of large numbers: Suppose you build an ensemble containing 1,000 classifiers that are individually correct only 51% of the ...
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1answer
81 views

Stacked Model performance?

I am currently working with a dataset that seems very easily separable and I have an accuracy of 99% for SVM (NN-98%, RF-98%, DT-96-97% and I have checked for leakage & overfitting). As part of my ...
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245 views

Stacking and Ensembling methods in Data Science

I understand that using stacking and ensembling has become popular, and these methods can give better results than using a single algorithm. My question is: What are the reasons, statistical or ...
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927 views

XGBoost Log Loss different from GridSearchCV Log Loss

I have a classification problem where I am trying to predict if the data returns a 1 or 0. So your classic binary classification. I have my set of data that I have split into the dependent variables (...
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57 views

How does bagging help reduce the variance

I learned that bagging helps reduce variance by averaging but I couldn't understand this. Can someone explain this intuitively?
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How do I Combine two CNN models (h5 format)?

I have two CNN models, both of them are trained on the same dataset. How do I combine/ensemble both to make predictions on test data? ...