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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23 views

How to add weight to a variable to a ensemble method

I am building an ensemble method and would like to add weight to one particular variable (x3 in this case). How can I do this? Here is my code: ...
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1answer
42 views

How are Decision Trees averaged in Random Forest?

We all know that a Random Forest is an ensemble of Decision Trees, whose results are averaged. Every source I find simply talks about "averaging trees", but how does this "averaging of trees" happens?...
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8 views

Manual setting of target variable based on features' minimum values: f1 score = 1

I am building a classifier for user engagement in my website. Basically, since there are no "proxy" for engagement, i.e. there is no pre-defined target variable, I came up with minimum thresholds ...
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1answer
36 views

Weighted Linear Combination of Classifiers

I am trying to build an ensemble of classifiers whereby I want my algorithm to learn a set of weights such that it can weight the outputs of different classifiers for a set of data points. I am ...
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0answers
29 views

How to deal with small amount of labeled samples?

I'm trying to develop skill to deal with very small amount of labeled samples (250 labeled/20000 total, 200 features) practicing on Kaggle "Don't Overfit" dataset (Traget_Practice have provided all 20,...
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0answers
9 views

Ensemble learning for multiple hypothesis classes

Just to confirm if the following description falls in the category of ensemble learning. Suppose given a training set $D=\{(X,Y)\}$ we are asked to train a regressor. But now the way we do it is to ...
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1answer
41 views

Understanding why ensembling only improves marginally

I have two models, A and B, trained on Imagenet. Their accuracies on Imagenet validation set are 35.6% and 28.64% respectively, while the accuracy of their ensemble (averaging their scores) is 35.68%. ...
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8 views

Would it make technical sense to model its two known subelements separately (CLV example)?

Let's assume Customer Lifetime Value (CLV) is defined as average basket x frequency. Option A is to build model predicting CLV ...
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1answer
35 views

checking model stability - Performance for different class

I tried to do multi-class classification problem. The goal is to predict whether the match will be won by HomeTeam, AwayTeam or Draw. I did feature engineering from the attributes and finally came up ...
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31 views

Recommendations for Neural Network Stacking Project

I embarked in a project to use a stacking ensemble of neural networks to perform a binary classification prediction. I have the ensemble running and making decent accuracy estimates (90% ca.). ...
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1answer
684 views

Is there any implementation of Extended Isolation Forest algorithm in R/Python?

I am using isofor package for regular Isolation Forest but I came by an article about Extended Isolation Forest and need your advise which package has this ...
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89 views

Ensemble Method using XGBoost and RotationForest python

How can I create an ensemble model using XGBoost and Rotation Forest in python?
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1answer
62 views

Voting classifier using grid search for Time Series

I have three models Arima Auto ARIMA Double Exponential Smoothing I want to apply ensemble method - voting method and allow the classifier to learn weights for these three models. I have checked ...
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1answer
33 views

Building Stacking machine learning model using three base classifiers

I did a stacking using three base classifiers RF, NB, KN N and metamodel random forest or SVM using sklearn library But which is strange each time i change the metamodel i got the same results. Is it ...
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0answers
32 views

Is linear regression on the trees of XGBoost (rather than taking their mean) useful/popular?

Given training data $(\underline{x}_1, y_1),...,(\underline{x_N}, y_N)$, one can choose a variety of ensemble method for trees. These algorithms output a set of trees $T_1, ..., T_n$, and then the ...
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1answer
74 views

Difference between bagging and boosting

Can anyone explain me the basic difference between bagging and boosting and which technique can be used in which scenario?
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1answer
2k views

Adaboost vs Gradient Boosting

How AdaBoost is different than Gradient Boosting algorithm since both of them works on Boosting technique? I could not figure out actual difference between these both algorithms from theory point of ...
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1answer
134 views

best activation function for ensemble?

i have created some logistic regression model (different preprocessing) with softmax function. and i mix all model with an ensemble with a hierarchical method. so the output of all model (base) will ...
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2answers
46 views

What methods can be used to detect duplicacy in image dataset?

I want to remove duplicate images from a dataset of 50Million images. What is the best method to detect all the duplicates? Do you think one shot learning is good for this?
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1answer
138 views

Why Extra-trees should only be used within ensemble methods?

I was reading scikit-learn documentation for Extremely Randomized Trees and I found this warning: Warning: Extra-trees should only be used within ensemble methods. Why is that?
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1answer
39 views

Combining outputs of ridge regression models?

I am facing an issue where I have 7 sets of different variables/columns/predictors. I am trying to predict same target variable and I want to observe the importance/effect of all the sets according ...
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0answers
26 views

Why should each layer's child network output be close to parent network's output for variance regularizer?

I am reading up on PEA (Pseudo ensemble agreement) regularizer. specificaly in the neural networks domain. It introduces the concept of perturbing the model a little and forcing the model to make ...
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0answers
243 views

Combining image and scalar inputs into a neural network

I'm looking at the best way of combining CNN with image input and a scalar value. I know that one of the ways is to concatenate flatten layer with this scalar value. But flatten layer consist for ...
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0answers
55 views

heterogeneous input features for estimators in sklearn.ensemble.VotingClassifier

I would like to do ensemble on my model. Two of them are SVM and XGBoosting. SVM could not tolerate null value and XGB can do it. So I have different features for each of them. but when ...
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2answers
800 views

Categorical data for sklearns Isolation Forrest

I'm trying to do anomaly detection with Isolation Forests (IF) in sklearn. Except for the fact that it is a great method of anomaly detection, I also want to use it because about half of my features ...
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2answers
208 views

What is the meaning of the term “pipeline” within data science?

Note: this question was asked and removed just before I posted my answer below, so am repeating the general idea here People often refer to pipelines when talking about models, data and even layers ...
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2answers
63 views

Why does prediction by a consensus of classifier work better than prediction by a single classifiers?

I have seen that consensus of classifiers (taking say 5 separate classifiers) and obtaining the final labeling of the unknown sample based on the voting method (whichever class gets the predicted the ...
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2answers
56 views

Ensemble models - neural network input both original data and predictions of other models?

From my understanding in order to improve accuracy with ensemble models you need a wide range of independent ensemble methods. I was wondering whether using the ouput of a random forest model as one ...
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0answers
61 views

Prevent overffitting in model stacking with training on the same target

I'm trying to solve Quora Question Pairs with model stacking. My first layers are: CNN trained to predict the same target as whole model should "Magic features" like question frequency in whole ...
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1answer
60 views

How to construct a self learning process for an ensemble model?

I have two different (regression) models spitting out predictions on a daily basis for the same dependent variable. My intention is to assign weights to those two predictions and calculate a weighted ...
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1answer
401 views

Geometric and harmonic means in ensembling methods

When using ensembling methods for regression, a common approach is to average (using the arithmetic mean) the outputs of the weak learners in order to obtain the output of the ensemble. Is there a ...
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2answers
4k views

How to avoid memory error with Pandas pd.read_csv method call with GridSearchCV usage for DecisionTreeRegressor model?

I have been implementing a DecisionTreeRegressor model in Anaconda environment with a data set sourced from a 20 million row, 12-dimensional CSV file. I could get the chunks off of the data set with ...
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3answers
112 views

What are the individual models within a machine learning ensemble called?

I am aware that an ensemble machine learning model is a stack of two or more machine learning models. Is there a word to refer to those individual models that go into the ensemble model? (i.e. a ...
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2answers
73 views

Questions on ensemble technique in machine learning

I am studying the ensemble machine learning and when I read some articles online, I encountered 2 questions. 1. In this article, it mentions Instead, model 2 may have a better overall performance ...
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1answer
82 views

Multi-dimensional regression with ensemble models?

I'm curious to know whether boosting, random forests or other types of ensemble models can perform multi-dimensional regression. To be precise: That means multiple outputs (multi-dimensional labels) ...
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0answers
36 views

AdaBoost - Ensemble model perform poor than best weak classifier

Can Adaboost's ensemble classifier perform worse than the best of the weak learners considered? If so when in what case of weak learner the ensemble learning does not perform better?
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1answer
23 views

How to approach model reporting task

I have been tasked to report on an ensemble model that was created in h2o which includes several model subtypes such as Random Forest, GBM, linear models etc. The end goal is to predict churn rates ...
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1answer
133 views

Retrieve user features in real time from UserId for prediction

Let's say I'm building an app like Uber and I want to predict the user's most likely destination based on the user's past history, current latitude/longitude, and time/date. Here is the proposed ...
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1answer
114 views

testing new data in model

I have ensembled 3 algorithms as below, ...
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2answers
82 views

Combining Different Models

I have N models, each of them are used to predict on a set of data. I am currently combining their predictions by averaging across rows. Need suggestions on combining their predictions. End goal is to ...
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1answer
24 views

Re: Missing Value

I have a large dataset that has session length records per user basis. And I am trying to predict the purchase behaviour based on the session length. But this data has multiple zero in the session ...
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2answers
605 views

Taking average of multiple neural networks?

I'm fitting a neural network using a very small data set, so try splitting the data into training and validation sets. (there is a separate test set) If I split training/validation randomly multiple ...
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2answers
146 views

How to create an ensemble that gives precedence to a specific classifier

Suppose that in a binary classification task, I have separate classifiers A, B, and C. If I ...
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2answers
97 views

Ensemble Model vs Normal model

If I get 95+ % accuracy in normal models, should I still consider Ensemble models? Why should I choose Ensemble models over normal models?
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1answer
125 views

Ensemble Probabilities of the different models

I have a Multiclass Dataset and I am getting probabilities of classes from RandomForest. However, I want to divide the dataset for each class as examples of either the case belongs to that class or ...
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1answer
53 views

How to average classifiers with AUC metric?

I am modeling a binary classification and my loss function is the gini function (normalized area under the curve). Here's my implementation: Split the data with k-folds Train k classifiers Now I ...
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2answers
73 views

Learning Algorithm that decide which model gives better results for each testing instance

Is their any existing Ensemble technique which uses subset of training data to predict which algorithm is better for predicting each instance of testing data? Let's say we have N sized training set ...
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0answers
693 views

How to tune weights in Voting Classifier (Sklearn)

I am trying to do the following: ...
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1answer
2k views

Sklearn Aggregating Multiple Fitted Models Into A Single Model? (binary classification)

My problem context: dataset too big to fit into memory. binary classification [0,1] 30 csv files in a directory with exactly 30,000 samples (rows) each file contains 15,000 ...
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1answer
2k views

How can I do tree_method ='exact' in XGBoost classifier?

I am doing XGBoost classification on a huge data set and its showing: ...