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 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
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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.
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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 ...
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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
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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 ...
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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 ...
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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 ...
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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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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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
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1
11-07-2014
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1
0
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1
15-07-2014
0
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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 ...
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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 ...
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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, ...
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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 ...
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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.
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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 ...
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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 ...
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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
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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 ...
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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 ...
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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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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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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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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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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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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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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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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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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 ...
