Random forest is a machine-learning classifier based on choosing random subsets of variables for each tree and using the most frequent tree output as the overall classification.

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Applying randomforest algorithm (fit) on new data without recomputing the fit [on hold]

I have a need to do realtime predictions for individual rows of data based on a previously computed randomForest algorithm. How can I run the "predict" command without recomputing "fit" on the entire ...
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Classification & Regression Trees (CART)

It seems that most CART examples I encounter involve splitting the sample into a training sample and test sample. This split sample method strikes me as terribly inefficient unless you have a large ...
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How to increase the performance of random forest classifier?

I have a text classification task. These are the metrics for different languages at present: class1: 0.6823 class2: 0.7450 class3: 0.66 class4: 0.6719 How can I ...
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25 views

Get bagging sets of random forest in scikit-learn

In scikit-learn each tree of a random forest is trained with a set of samples drawn with replacement from the training set. To do some analysis of a trained forest, it would be nice to know, which ...
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The absolute value of importance is not very helpful, than how to calculate the relative importance of variables in a random forests Regression [duplicate]

I have used a random forest model for regression analysis. Now, I am having difficulty in working out what can be used in measuring variable importance. The importance function provides the mean ...
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40 views

Random forest regression on a given interval

I'm training a random forest regression model on a dataset that consinsts of values in the range of 0-50. It has many values close to zero and only 500 observations. The R^2 is also small, about ...
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29 views

prototypes and replacing missing variables in Random Forests algorithm

I'm new with Random forests Classification algorithm, and I have some questions about concepts confused me, What is the role of prototypes in the classification operation, Are they the core of the ...
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25 views

Highly correlated variables in random forest

In my understanding, highly correlated variables won't cause multi-collinearity issues in random forest model (Please correct me if I'm wrong). However, on the other way, if I have too many variables ...
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35 views

Different results from randomForest via caret and the basic randomForest package

I am a bit confused: How can the results of a trained Model via caret differ from the model in the original package? I read Issue on prediction with FinalModel of RandomForest in R using the CARET ...
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Random forest in matlab: questions about OOB error in TreeBagger [migrated]

I'm currently working on a classification/regression problem with random forests and using Matlab's TreeBagger. I want to estimate the performance of the model for the two different classes(positive ...
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35 views

Unsupervised Clustering using randomForest

Outline of clustering technique using Random Forest A synthetic data is created by randomly sampling from the data of interest. It is used as the base line to measure the "structureness" or ...
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40 views

Classification when some classes are dependent

I think my problem can easier be explained via an example: Assume we have a dataset containing the images of 10 different mammals, let's say lion, elephant, cat, ... and horse. We have a 20-class ...
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71 views

How to document a Random Forest result (final model)?

Using Random Forest to predict dichotomous variables (for classification) I encountered the problem how to best document this model, i.e. I want the user to reproduce/use the final model I created on ...
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33 views

RandomForest Classifier With Very High Success Rate

I'm having a weird problem that may suprise you all. My classification rate is too high on my test set. I'm using scikit-learn packages, and I'm very suspicious of these classification rates, as they ...
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48 views

partial plots from a Random forest classifier for binary predictors

I'm working on a classification problem with continous and categorical predictors with Random Forests (RF). I'm particularly interested on RF as we avoid the specification of the functional form. ...
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55 views

Should I use Mean Square Error or Classification Rate?

I am a self-taught person and I would like your help. I am learning about predictive modeling in general, and I'm also trying to do predictive modeling for a specific problem. I am exploring ...
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25 views

Correlation between decision trees

How do you find the correlation between decision trees? I know this is an issue when working with random forests, but I can't find an explicit formula anywhere. I only get that random forest reduces ...
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24 views

Incorporating systematic error in (spatial) predictive modelling

I have created a model (random forest) and withheld 20%. When I apply the model to the withheld dataset and check the residuals against the real values I can see there is a systematic error e.g lower ...
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Can I safely use variable importance of a random forest in a paper?

Background: I just started with machine learning and I'm considering using it on old data based on which I'm writing a paper. The paper deals with radiation-induced lung damage and the data comprise ...
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23 views

Should I normalise (to sum to 100%) the MSE reduction for variable importance in a random forest?

I am investigating the importance of a set of variables in a linear model. I am conducting a random forest analysis and using the permutation-based Mean Square Error (MSE) reduction as a measure of ...
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21 views

Usage of standard deviation in random forests regression (for expected improvement)

It seems to me that the awareness of this problem is not high enough. Often the standard deviation (so the sd from each prediction of each tree in the forest) from the random forest regression is ...
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57 views

In recursive feature elimination in random forest, why are all features selected?

I am trying to use the recursive feature elimination in caret package. Here's the code; ...
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11 views

Adapt sampling in Random Forest (R)

I have a number of objects ($a,b,c,...$) which are observed over time. From each object there may be multiple observations (e.g. $a_1, a_2, a_3, b_1, b_2, c_1, ...$) with a numeric outcome. Note that ...
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Whats is meant by proximity in random forests?

I came across the term proximity in random forest. But I couldnt understand what it does in random forest. How does it help in classification??
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Understanding the (multiple) OOB columns in R's randomForest output

This is somewhat related to this post from 2012, however I want to focus specifically on the metrics generated while the randomForest model is running and make sure ...
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43 views

What do these decision boundaries indicate in random forest and svm?

I was working on data science harvard homework problem. It is a two class classification problem in which they plot the decision boundary for random forest, svm and decision tree. The problem has 2 ...
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determining how “important” a feature is in predicting a target in decision trees

Random forests allow us to compute a heuristic for determining how "important" a feature is in predicting a target. This heuristic measures the change in prediction accuracy if we take a given ...
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what is the difference between bagging and boosting in random forest?

I understand what is bagging and how it is applied to random forest. But how is bagging different from boosting. If boosting is different from bagging, how can boosting be applied to random forest?
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Predictive Model - Increase Pediction Accuracy for Less Likely Events

I am trying to build a model that predicts the which binary category a respondent belongs to (0 or 1). I have demographic variables (all categorical) and a few 10 point questions. I have built a few ...
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58 views

How does caret handle factors?

I have been testing conditional trees and random forests with caret, and I've noticed it does something weird with factors. So, for example, a ctree using the base dataset ...
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Why is MeanDecreaseGini over 1 in RandomForest package in R? [duplicate]

I am using R package randomForest, and calculated MeanDecreaseGini as below. ...
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42 views

How to do ranking with scikit-learn random forest model

I have a training dataset that I've developed, that has the following format: ------------------------------ | User ID | Item | Label | ------------------------------ | 001 | umbrella | 0 ...
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69 views

Deep Learning Predictions Seem Off

I'm doing a linear regression using the h2o deep learning interface with R. I'm comparing the predictions to the ones I'm getting from the randomForest R module. The predictions from randomForest ...
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31 views

using training data in final model output

I have customer data for around 400,000 customers where 270,000 of them are current customers and 130,000 of them are past customers who churned, what I am doing is classifying them as 0 (non-churn) ...
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51 views

Machine learning/random forests with noisy response data

Machine learning techniques like random forests seem to assume that the responses in the training set are known perfectly. Specifically for regression applications, it seems one needs to account for ...
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Splitting criterion in Model-based Recursive Partitioning

I read Achim Zeileis's paper: Model-Based Recursive Partitioning for a long time. But I still confused with the splitting criterion in this paper. My understanding is that it would evaluate the ...
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27 views

How to get class probabilities for unsupervised random forest

I have created random forest for the unsupervised case. g = randomForest(iris[,-5],keep.forest=TRUE) Now I need to know the class probabilities for each entry ...
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62 views

Penalizing to prevent overfitting

I am currently working on a decision tree algorithm. As you might know, decision trees, as you add more inputs/nodes can get very specific, which although makes them good classifiers, also gives them ...
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would adding the probabilities in a dataset be more accurate than the individual results?

Say I have the titanic kaggle competition, but I'm not interested in the competition for predicting survival for each individual. Instead I want the most accurate estimate of total survivors on the ...
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Can you add the probabilities of a classifier to better predict an outcome? [duplicate]

Say I am interested in predicting the TOTAL number of people that survive the titanic disaster, NOT each individual who died. Is it possible to run a probabilistic classifier on the data getting a ...
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105 views

Is a negative OOB score possible?

I'm currently implementing scikit-learn's RandomForestRegressor in Python and am scratching my head over why I have occasionally wound up with negative out-of-bag scores from it. As far as I can tell ...
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CV for model parameter tuning AND then model evaluation

I have a basic question on using cross-validation for model parameter tuning (model training) and model evaluation (testing) similar to this Model Tuning and Model Evaluation in Machine Learning I ...
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78 views

Classification problem-Big Data and simple decision rules: logit regression, LDA, random forest, cond. trees, or something else?

This is a big data question from someone who is more accustomed to small data. I would like to develop some classification "rules of thumb," that is, some simple decision rules or a decision tree ...
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root cause analysis with random forests?

There are metrics how to determine the most important features in a random forest model (Gini index, permutation accuracy). But is there also an approach how to analyse or visualize the root cause ...
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40 views

increase the speed of random forest conditional importance from the party R package

I have a dataset with numerical and categorical variables and a binary output variable. I want to use the conditional importance in random Forest This is my code ...
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RandomForest - why isn't it predicting well with manually-selected test sets?

I am using python sklearn.ensemble to do a RandomForestClassifier on about 800K rows of data, coupled with sklearn.cross_validation to generate the train/test sets. When it completes, it says on the ...
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72 views

random forest modelling with high dimensional data

I am puzzling on developing random forest regression of high dimensional data. My predicted variable is plant cultivar or Class (say 1, 2, 3) and regresser are 82 variable in separate column (40 X 83) ...
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37 views

Response Functions in a Random Forest

I am reading a chapter about random forest in a textbook. After the section about the predictor importance, the author introduces "Response Functions" as follow: "Predictor importance is only part of ...
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79 views

How to interpret random forest importance numbers

I ran randomForest in R package using 7 predictors variables (x1 to x7). I repeated the test with 4 dependent variables (y1 to y4). The importance numbers (IncNodePurity) are plotted in following ...