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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A good description of the random forests method

Can anyone suggest a good book or article describing the random forests method of classification? I'm not satisfied with the way the subject is treated in "An Introduction to Statistical Learning with ...
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36 views

Random Forest Overfitting R

I used a two-step cforest in my model. the accuracy of the train set is 87%, yet the accuracy of the test set is 57%. This indicates the model is severely overfitting. How to solve this problem? ...
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Random forest - n variables forecast from the same n variables

I have observed values for $X_1, ..., X_n$ variables. I would like to forecast $X_i$ based on $X_1,...X_n$ using random forest. I will be building random forest for each variable. I am new to data ...
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Deviance explained in classification randomForest model?

Is there a way to estimate deviance (or variance) explained from a classification model in randomForest? I've seen "deviance remaining" reported for presence-absence random forests in the SDM ...
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11 views

Multiple Response Regression in Spark MLLib

I am trying to do a regression using RandomForests in Spark ML where I have several input variables and would like to predict several responses. Training data would look like X = ...
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15 views

Reporting variable importance results using cforest

I have a large data set of odour samples. I used cforest and varimp to determine the most important variables (ie chemical compounds) in identifying sex. How do I report this in a paper? Do I need to ...
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1answer
16 views

plot only out of bag error rate in random forest

When I run random forest in R package and use plot function, there have many curves including out of bag error rate curve (black colour) and the misclassification error rate curves (other colours). ...
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28 views

How to report confusion matrix of Random Forest classifier on test set using R? [closed]

First of all, I have to say that I'm newbie in working with R. Anyway, I'm going to apply Random Forest classifier on my data set using R. To do this, I beforehand divided my data set into training ...
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74 views

Random forest in R using unbalanced data

I'm trying to build a Random Forest classifier in R that will identify people with a diagnosis. In the ecological setting (medical examination) there will probably be a rough 50%/50% proportion, but ...
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21 views

question about random forest accuracy

The conditional random forest model I use generates 44% of accuracy in general, I accidentally added one independent variable that did not exist on my data set. There is an error when I ran the model ...
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33 views

Does removing mildly correlated features (0.5) improve performance in predictive models? (SVM, random forests)

I am trying to model a binary response using a 500+ dataset. I already removed many non useful features in order to reduce dimensionality and improve my model. I am wondering whether in general ...
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Miss Forest & Iterative PCA : How to handle very sparse matrix imputation?

I am currently benchmarking matrix completion methods (k-NN, RandomForest and iterative PCA) on multivariate normal data in which I introduce a certain proportion of NA (5 to 95%). My performance ...
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1answer
37 views

WEKA: Visualize combined trees of random forest classifier

I have a small data set consisting of 385 entries and around 200 attributes. Because I want to apply attribute selection and because of the limited size of my data set, I got the advice to use the ...
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Comparing CV Predictions across Folds for Random Forest

I have implemented a k-fold cross validation to to assess the classification performance of a Random Forest. What I want to know is: are the predicted values across folds directly comparable? For ...
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1answer
19 views

A priori parameters to Random Forest based on n and p

If cross validation is too costly to determine the number of trees and number of max_features is there any standard to what you choose based on n and p. I know sqrt(p) is standard for max_features but ...
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What are the votes in R's unsupervised random Forest?

I’m trying to better understand unsupervised random forests. An important part of understanding unsupervised random forests is being able to assess how good / appropriate a given forest is. For ...
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1answer
23 views

How to use external data?

I'm building the model using the internal data to predict the health situation of the customer. I've just found about 100 new "external data" in the form of region data. Based on this new information, ...
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2answers
50 views

Handling case weight in the Random Forest packages in R

I checked both the randomForest and the rfsrc packages in R, but couldn't find an easy way to apply observation/case weight when ...
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9 views

How many samples do I need for OCR problems?

I am thinking about collecting samples of hand written digits (0 to 9) from people. I'll try to test different algorithms for optimal character recognition- some form of neural network and random ...
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26 views

Random Forest: Class specific feature importance

I'm using the bigrf R-package to analyse a dataset with ca. 50.000 observations x 120 variables, classified into two groups. After growing a forest of 1000 trees, ...
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63 views

Advantages and disadvantages of machine learning hyperparameter optimizers [closed]

What are the respective advantages/disadvantages of the following optimization algorithms for ML applications? (that is, to optimize the hyperparameters of a SVM, RForest, Boosting model, etc.). In ...
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53 views

Interpreting output of importance of a random forest object in R

After running a Random Forest Classifier on the Iris data set, I get an output that looks like this: ...
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1answer
53 views

R train random forest for positive or negative predicitve value, not accuracy

I am working with random forests on financial data (predicting if stock rises versus falls). I figured out that I get better performance, if I build one model for "rising" and one for "falling". ...
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34 views

Implementing random forest to predict the success rate and time of completion

I have a large dataset with more than 120 columns for which I'm trying to classify whether the order will be successful or not. There are two parameters that I need figure out. One is the probability ...
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1answer
59 views

why bootstrap result in overfitting for randomForest prediction?

I am dealing with an imbalanced dataset with the R package randomForest. Some one has suggested that, Bootstrap your data while over-sampling the rare class and under-sampling the typical class. But I ...
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327 views

Can Random Forest be used for Feature Selection in Multiple Linear Regression?

Since RF can handle non-linearity but can't provide coefficients, would it be wise to use Random Forest to gather the most important Features and then plug those features into a Multiple Linear ...
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71 views

Training a random forest in R with a maximum false positive rate

I ran the following code in R: rf.classifier.master <- randomForest(my_response ~ ., data=feature.matrix) print(rf.classifier.master) and got the following ...
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31 views

Validation: Random Forest Features selection

Context: I have a training dataset with 10000 features and i have selected the most important through a Random Forest. I used my subset dataset to train a Neuronal Net. Problem: When i use the ...
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1answer
41 views

RandomForest MeanDecreaseAccuracy interpretation

I know there are already some questions regarding the interpretation of the MeanDecreaseAccuracy metric of the randomForest-package, but it's still unclear to me. My assumption was that each variable ...
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1answer
31 views

Size of terminal node in decision tree/random forest?

I am having issues trying to understand what does the size of a terminal node in a decision tree means? Could anyone give me an easy explanation? I know a terminal node is a leaf node, the one that ...
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2answers
67 views

Help evaluating a model in R

I am a newbie in R and I am trying to do my best to create my first model. I am working in a 2- classes random forest project and so far I have programmed the model as follows: ...
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1answer
69 views

Creating a test set with imbalanced data

I am working on a binary random forest using R. mu data set consists of 300 cases classes 1 and 2100 cases class 0. I am planning to evaluate my model using the model prediction and the AUC and for ...
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1answer
27 views

Random Forest and missing values in numeric features

I'd like to use a random forest for predicting how long a person will stay a customer of our company. One feature I'd like to use is the average age of the customer's kids. The problem is some ...
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1answer
46 views

Random forest: how to derive the characteristics of the predicted classes?

Decision Trees stratify the feature space into different regions and fit a basic model to each region (average for regression, most frequent class for classification). With this method it's not ...
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1answer
44 views

How do I implement missing value patterns?

I have a training data set and I was able to find some interesting patterns in the missing values, and I used binary variables in order to represent the missingness. I am going to train a model, say a ...
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52 views

Random Forest, dependent measurements

I have a following quiz: A random forest is used for classifying the disease state of patients based on measuring multiple genes. The dataset consists of 100 genes and 50 patients. However, ...
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42 views

In a random forest, is larger %IncMSE better or worse?

Once I have built a (regression) random forest model in R, the call rf$importance provides me with two measures for each predictor variable, ...
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96 views

Random Forest - Numeric and Dummy Variables together

I am trying to create a logistic regression model and a random forest model on the same data to predict probability of default. For the logistic regression model, I have created some dummy variables ...
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135 views

What measure of training error to report for Random Forests?

I'm currently fitting random forests for a classification problem using the randomForest package in R, and am unsure about how to report training error for these ...
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147 views

Does modeling with Random Forests require cross-validation?

As far as I've seen, opinions tend to differ about this. Best practice would certainly dictate using cross-validation (especially if comparing RFs with other algorithms on the same dataset). On the ...
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1answer
39 views

What are the goodness of fit variables in classification trees?

I have used multivariate linear regression for one of my projects, and used r-square and p vals to evaluate the model. I couldn't find what such metric we would use for decision trees and random ...
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1answer
54 views

How does feature selection work in Random Forest?

I've been trying to improve the performance of my random forest model, and read the following paper on feature selection using random forest (see algorithm in section IV: Overfitting - A. Feature ...
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1answer
82 views

Interpreting % Var explained in Random Forest output

I've run a Random Forest in R using randomForest package. The fitted forest I've called: fit.rf. All I want to know is: When I type 'fit.rf' the output shows '% var explained' Is the % Var ...
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Random Forest Instance Proximities Training to Test

Using randomForest, I want to create a low-level projection of the instance proximities, as produced by MDSPlot(). However, I ...
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10 views

Sparse Categorical predictors with Categorical Response

I have a huge set of sparse Categorical predictors (500+) and a binary response variable, Dataset is mostly sparse. I tried using Random Forrest with very little success. Any suggestions?
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Classification with randomForest in R - always predicts 0

I have a data frame of approximately 11500 records. Each row of the frame has a response variable, isfastes, and a number of other predictors (size, cores, ...
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31 views

Is random forest a right approach?

I am pretty new to stat and having read some articles about applying random forest algorithm. I have a employee survey return which contains approx 200 questions say 50 questions per a theme so there ...
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41 views

Regression Tree Predictions

For various regression tree algorithms (e.g. GBM, Random Forest, Extra Trees), is there any sensible way to get predictions for new data when the independent variables for the new cases are much ...
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19 views

Fine Tune Model with Caret Package

I am using CARET package to fine tune random forest mtry parameter. In the package, tunelength parameter can be used to automate search for best mtry parameter. But the problem is the "tunelength" ...
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27 views

Classification Model on Single Feature?

this is my first time using StackExchange so forgive me if I commit any faux paus with this question, and it has only been a few months since I first started learning machine learning. In my ...