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31 views

Boosting Explained

I'm a newbie trying to learn Boosting. The examples I found online are quite confusing. Is there a simple tutorial somewhere that explains ...
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1answer
22 views

Skewed Classification Problem

So I've read around and seen this is a problem. I have a classification problem and 12 variables ... I'm working on getting more, but even if l get the number to 20-30 I feel like the problem will ...
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1answer
61 views

Buiding Ensemble model

I'm new to ensemble model. Suppose I've KNN models like this - (in R) library(class) ...
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3answers
37 views

Comparing SVM Model

Pardon my understanding of SVMs as it is very little. We often hear of ensemble classifiers and stuff like this. Say if i were to have 3 different SVM Models for the same dataset predicting a ...
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1answer
71 views

Minimizing Curve fit for predictive model

Let's assume we've found 100 independent variables that can predict y. Each of those independent variable are close to uncorrelated and they are all curve fitted. Using any single one to predict y ...
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0answers
37 views

Model Stacking algorithm

I'm trying the stacking method to see if it improves my results, but before using some R package, i decided to code it by myself. Here's a pseudocode of what i'm doing: ...
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3answers
133 views

Ensemble of different kinds of regressors using scikit-learn (or any other python framework)

I am trying to solve the regression task. I found out that 3 models are working nicely for different subsets of data: LassoLARS, SVR and Gradient Tree Boosting. I noticed that when I make predictions ...
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0answers
52 views

Why do Matlab's TreeBagger and fitensemble with 'bag' and same parameters give different predictions

Matlab's Statistical Toolbox has two bagging tree algorithms implementation: Tree Bagger Fitensemble (see 'Bag' method) I am currently using (1) for a Regression problem. However, I would like to ...
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0answers
28 views

adaboost with multiple classification algorithms

Up to now I saw that all adaboost implementations use single classification algorithm and a training dataset as input and then creates multiple classification models by re-sampling dataset and uses ...
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0answers
77 views

Combining multiple feature subsets through ensemble classification methods?

I have a set of $N$ samples to be classifies in a binary classification problem. I have extracted features from these samples from 4 different perspectives (views) of every samples. Hence I have 4 ...
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1answer
94 views

what is the effect of bootstap resampling in bagging algorithm(ensemble learning)?

In ensemble learning with bagging, why is it important to do bootstrap resampling (sampling with replacement) instead of just sub-sampling (sampling without replacement)?
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1answer
36 views

Confused about cross validation for model stacking

I'm reading section 8.8 of Elements of Statistical Learning, and though I keep reading the section on calculating the ensemble weights I'm missing something. It says that the stacking weights are ...
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0answers
26 views

What techniques are used to prevent overfitting in DSN

A Deep Stacked Network (DSN), is a ensemble learner, which roughly works by training a single hidden layer neural network on the inputs and target outputs, then training another which takes an input ...
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0answers
77 views

credit scoring - fraud scoring

I have been asked to build a credit scoring model and we are relying on several Machine Learning API, in order to build our feature vectors. One of these API is MinFraud. However, as they provide us ...
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0answers
33 views

What's the potential reason that by combining two feature sets the performance of random forest dropped?

I am building random forests on high dimensional, sparse, and class unbalanced training datasets (around 500 - 5000 examples) using two different feature sets. I did stratified 10-fold cross ...
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0answers
20 views

Assessing predictor contribution to model output

Many of machine learning methods are considered as "black boxes". Examples of such methods are SVM, Neural Networks, Random forests etc. One may apply sensitivity analysis techniques (as described for ...
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2answers
88 views

Analysis for checking if an Ensemble model is a better fit for a dataset than Primitive model

I have a dataset and have the option to apply either GLM (primitive) or a Random Forest (ensemble). So far the Random Forest is giving way better results than the GLM. As it is generally believed that ...
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0answers
15 views

In ensemble predictions, what is reliability diagram?

I have recently heard the term reliability diagram used with regards to the analysis of ensemble predictions. What does it show and how is it calculated?
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1answer
34 views

What statistical analyses should one perform on ensemble forecasts (given a measurement)?

I have an ensemble of time-series predicting a scalar variable. Additionally, I have a measurement time series of this scalar variable. Which statistical analyses could and/or should I perform to ...
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1answer
138 views

Suggestions needed about classifier fusion

I'm working on a classification problem which involves two classifier to observe a single event. I'm providing a high level description of the problem without going into the technical details (the ...
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0answers
32 views

Polling vs averaging in Random Forest models

Why is it that for Random Forest we take the average vote from each classifier in the ensemble rather than the average probability from each classifier in the ensemble? Is there theory behind why ...
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0answers
17 views

Proper way to stack models when some models aren't always applicable?

Suppose you have two (or more) models that you want to ensemble together. However, some of the models are trained specifically on very specialized subsets of your data. If you do the stacking with a ...
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0answers
110 views

Stacked Generalization Ensemble Algorithm for regression

I am using stacked generalization(Rupert 1992) for combining multiple(8) heterogeneous base learners for regression. What I understand from the pseudo codes that Train the 8 learners on 8 instances ...
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0answers
38 views

Ensemble model performs better with worse performing consitutent models?

I have a forecast model I am developing that uses some very unreliable input data, missing data (due to sensors or comms failures) is the rule, not an exception. The quantity being forecast is a daily ...
3
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1answer
276 views

Why not always use ensemble learning?

It seems to me that ensemble learning WILL always give better predictive performance than with just a single learning hypothesis. So, why don't we use them all the time? My guess is because of ...
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1answer
49 views

What is this ensemble learning technique called?

For example, I trained two models: one with SVM and one with KNN. Final Prediction = 0.4*KNN + 0.6*SVM Is this considered blending?
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1answer
402 views

What is the equation for random forest?

I need an equation for random forest so that I can score fresh data I receive every week, based on beta estimates I got after building model using this ensemble methodology. Every week I do not want ...
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1answer
28 views

What method to use for cluster identification ?

This question is from a confused novice. I have a data set with where each point is located in a 2-D space defined by two objectives (say, X and Y). I wish to identify a set of points from this space ...
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2answers
219 views

Ensembles of Ensembles?

I like the idea of ensemble learners, especially Bagging, but I always wondered as why they are not the most powerful learners since they have a clean motivation. I don't have the answer to that ...
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0answers
25 views

combining classifiers trained on different data

Could you please recommend methods for combining classifiers, which were trained on different patients? For example, there are 10 patients and for each of them I trained a binary classifier. Now I ...
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1answer
311 views

k-fold Cross validation of ensemble learning

I am confused about how to partition the data for k-fold cross validation of ensemble learning. Assuming I have an ensemble learning framework for classification. My first layer contains the ...
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2answers
222 views

Combine decision trees from GBM to reduce output

I am curious if any research has been conducted to efficiently combine trees resulting from a gradient boosting process. I routinely run a process that generates 20 or 30 thousand trees in R. I then ...
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0answers
77 views

How to combine predictions in ensemble

I am trying to learn more about how to build ensembles of predictions in R and coming to a roadblock, and am hoping one can offer guidance. I often read about people automatically identifying how ...
1
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0answers
77 views

What are the latest methods to generate ensembles?

I am working with ensembles, and I'm willing to go deep inside the work. I have historical records of: Observations of one variable Historical forecasts for the same variable For future ...
3
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2answers
81 views

Combining models for prediction based on residual performance

I have never read or seen someone do this before, so I wanted to pose the question here. Suppose I fit a basic linear model, $\text{price of house} = \beta_0 + \beta_1*\text{taxes} + ...
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2answers
87 views

Ensemble of models with different feature spaces

BACKGROUND I have data in which the dependent variable is binary with a highly-skewed distribution: <1% records are 1 (doers), >99% records are 0 (non-doers). I'm using logistic regression to ...
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0answers
62 views

Dissimilarity with earlier features part of cost function

I am using a RandomForest on features (pixels) of images, and I am considering adding cost for "similarity to already other included features" to the cost function. Imagine you have a current RF ...
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3answers
1k views

Can Random Forest Methodology be Applied to Linear Regressions?

Random Forests work by creating an ensemble of decision trees where each tree is created using a bootstrap sample of the original training data (sample of both input variables and observations). Can ...
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2answers
189 views

Why are Random Forests splitted based on m random features?

I was watching the following tutorial on Random forests and it says that, "at each node, choose random subset of m features and only consider splitting on those features" I do not understand why we ...
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0answers
89 views

Proper splitting of data set for Ensemble methods

I have 10,000 documents. Each document has a label ($Y$) that is either $0$ or $1$ (the 0-1 split is pretty much 50/50 over my 10,000 documents). Each document has 10 fields. Each field can have any ...
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1answer
171 views

Do ensemble techniques increase VC-dimension?

Techniques like Adaboost use a ensemble of weak classifiers to obtain a "better" classifier. Does(Can) the final classifier have a greater VC-dimension than the weak classifier? An intuitive ...
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2answers
151 views

Gradient boosting algorithm (steps) question

So, far I have read following regarding boosting: Boosting is an ensemble technique. Train learner sequentially, where early learners fit simple models to the data. Analyze data for errors, that ...
1
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1answer
459 views

Matrix Factorization Model for recommender systems how to determine number of latent features?

I am trying to design a matrix factorization technique for a simple user-item, rating recommender system. I have 2 questions about this. First in a simple implementation that I saw of matrix ...
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1answer
87 views

Using a logistic model on the estimates of several other classification models

I'm working on a classification model that will predict whether a sales opportunity will end up 'won' or 'lost', given various attributes of the opportunity. I've been using my training data to build ...
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1answer
137 views

Is there a well-defined class of ensemble methods?

Ensemble methodology's main aim is to somehow aggregate or summarize estimates from multiple models. In some cases this is aggregating different bootstrap estimates or Monte Carlo estimates, but ...
9
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1answer
822 views

Using LASSO on random forest

I would like to create a random forest using the following process: Build a tree on a random samples of the data and features using information gain to determine splits Terminate a leaf node if it ...
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2answers
467 views

On combining SVMs

Suppose we have a supervised training set $T=\{ (x_1, y_1),\dots, (x_n,y_n)\}$ where $x_i$ is an example and $y_i \in \{-1,+1\}$ is its label. Further suppose that examples are only observable through ...
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0answers
78 views

One-against-all probability values into a multiple class probability value?

I have a 10-class classification problem. I've approached the problem as a set of one-against-all binary problems. For each class I've built a MLP neural network that provides a probability estimate ...
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3answers
206 views

Limits to tree-based ensemble methods in small n, large p problems?

Long time grazer, first time poster. I'm hoping to gather people's opinion on the following: Tree-based ensemble methods such as Random Forest, and subsequent derivatives (e.g., conditional forest), ...
6
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3answers
780 views

Ensemble time series model

I need to automate time-series forecasting, and I don't know in advance the features of those series (seasonality, trend, noise, etc). My aim is not to get the best possible model for each series, ...