Random forest is a machine-learning method based on combining the outputs of many decision trees.

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Random forest low score on testing data (scikit-learn)

I am trying to train my model using Scikit-learn's Random forest (Regression) and have tried to use GridSearch with Cross-validation (CV=5) to tune hyperparameters. I fixed ...
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randomForest MDSplot help R

I am new to R and randomForests so bear with me. I am trying to visualise my randomForest a little better using the MDSplot() function in Random Forest. There are two things i would like to do, and i ...
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number of trees that were built without minority class?

Lets assume that my random forest has 500 trees. My data is imbalance with 90% of class A and 10% of class B. I am wonder if there is any way to calculate roughly the number of trees that are built ...
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37 views

Confused Scikit results

I am doing classification machine learning on a particular dataset on which an SVM model (using Scikit.learn) is giving a Matthew's correlation coefficient (MCC) of ...
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14 views

How to deal with data in which users_ids belong to more than one category (Multilevel) using Random Forest?

I know it sounds trivial, but I could not find any ready answers for this. Suppose we have this kind of data and we want to predict some target values for several users. ...
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36 views

Random forest, cross validation or out-of-bag error?

I am training a random forest on a text data set (that I represent with synthetic features) and I am willing to assess the quality of the features I am creating. So far, I focused on the out-of-bag ...
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46 views

Is decision tree output a prediction or class probabilities?

A Random Forest works by aggregating the results of many decision trees. Recently, I was reading about how the RandomForest aggregates the results, and it made me question whether the results from ...
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28 views

Difference in H2O distributed RF and randomForest package in R [closed]

When I built RF using H2O tool and randomForest package in R, there was significant difference in performance (binary classification, KS was 56 and 32). This performance was measured in test data. I ...
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56 views

Weighting time series data for prediction

I am building a simple random forest to predict soccer results in sckit. I simply train the model to predict each teams score based on various features. However I am trying to think how I can weight ...
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37 views

How to interpret my learning curve

I created the following learning curve in order to diagnose my Random Forest model. As I can see the curve indicates high variance and 'underfitting' (not overfitting), because cross-validation ...
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49 views

normalization to zero mean and variance one logistic regression & random forrests

i was just thinking how does normalization to 0 mean and variance 1 (using an affine linear mapping) can impact the classification accuracy and the choice of hyperparameters when using: logistic ...
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24 views

grouping attributes in RF and GBM

i have a dataset with 1000 samples and ~11k features (SNP markers). i have identified 100 additional binary features describing the markers themselves so i have a ...
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40 views

Is Random Forest suitable for very small data sets?

I have data set comprising 24 rows of monthly data. The features are GDP, airport arrivals, month, and a few others. The dependent variable is number of visitors to a popular tourism destination. ...
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18 views

Performance evaluation measures for binary random forest classifier

I am using Random forest (Matlab) to classify the binary data. Broadly, the input to the random forest is number of features and class label. And random forest, after training, return the labels for ...
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27 views

Is it possible to do random forest with multiple responses or combine such ensembles for multi-label classification?

I have a dataset which has numerous nominal responses, and many predictors. Each response is basically a pass/fail check of a certain test applied to the data. Multiple methods are applied to each ...
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55 views

When to use regression trees/forests?

As I was looking for a fine regression algorithm for my problem. I found out one can do that with simple decision trees as well, which is usually used for classification. The output would be something ...
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46 views

SVM Vs Neural Network Vs Random Forest classifier comparison on multi class problem

Any idea if SVM or Neural Net or Random Forest works better on a classification problem on the same multi class dataset? I mean, in general, which should outperform the comparison?
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30 views

What happens to multi-category variables in algorithms like Random Forest that sample the feature space?

Suppose I have a multi-level categorical variable like color (say, with 7 levels). Some software libraries only allow numeric matrices to train models, so we need ...
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101 views

Can we make Random Forest completely interpretable by fixing the seed?

Say I want to "visualize" in some way Random Forest (or make it implementable). All of my points come from the idea of fixing the seeds. Let $z_1$ be the seed in the creation of boostrapped traning ...
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53 views

random forests for optimal variable selection/feature selection

Gurus, I just came across this tutorial (http://blog.datadive.net/selecting-good-features-part-iii-random-forests/) about using "random forests" for optimal variable selection/feature selection. The ...
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18 views

Predict count data for unsurveyed areas

I am looking to predict count data from deer surveys for the unsurveyed areas. I want to make these predictions based on vegetation type and size of the vegetation type (acres). I started by using ...
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45 views

Which model should I use to predict pass/fail scenario?

I am new to predictive modelling. I am unable to choose the correct model for predicting if a student will pass or fail a particular exam. My data set : Input variables: Total_tests_Taken , ...
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34 views

Classifier performance difference

I am using SVM and Random forest for classification purpose on a dataset. I am able to optimise the SVM parameters and SVM is providing very good performance in terms of accuracy, recall. But,at the ...
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101 views

Where in Elements of Statistical Learning does it talk of a “trick” to deal with categorical variables for binary classification?

I've struggled to deal with categorical variables in Random Forests, for binary classification. Between 8:15 and 9:30 in this instructional video, it talks of a "clever trick". It says the trick ...
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What is the difference between oob (out of bag) error and (1 - accuracy) in RandomForest?

In a Random Forest, I know that the Out Of Bag Error is described as the fraction of number incorrect classifications over number of out of bag samples. Accuracy is defined as the number of correct ...
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27 views

numerical attributes with random forest

I am working on a problem of COPD exacerbation likelihood prediction, I have a total 62 attributes out of which 38 variables/attributes are of numeric/continuous type and remaining are either binary ...
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68 views

randomForest vs randomForestSRC discrepancies

There are two popular R packages to build random forests introduced by Breiman (2001): randomForest and randomForestSRC. I am noticing small, yet significant discrepancies in terms of accuracy ...
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Classification when training set contains missing/unknown class labels

I have a set of data points that belong to two classes - class A and class B. I know that a subset of the data points belong to class A. But I have no idea of the classification of the remaining data ...
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59 views

Decision Trees and Regression - Can predicted values be outside range of training data?

When it comes to decision trees, can the predicted value lay outside of the range of the training data? For example, if the training data set range of the target variable is 0-100, when I generate my ...
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19 views

Probability distribution in RandomForest

I'm trying to build a model with RandomForest in R, but I'm facing some issues. I'am using: ...
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25 views

Help: Random Forest optimization (image classification)

I'm having trouble classifying images using a random forest. The images all have a very similar scale, but they may be rotated arbitrarily around a fixed point in the image. The core problem is ...
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31 views

restrict splitting variable number in random forest?

Background: I have a set of ~100 features (input) that predict 25 variables (output). My input variables are integers in {1,2,3,4,5,6,7}, my output is continuous. I have ~100K data rows available. I ...
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Using MICE with Random forests taking into account clustering

I am using the mice package in R to create multivariate imputed datasets. For this, I am using mice(data, meth= "rf")function. I ...
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28 views

Difference in execution times between caret and randomForest (even with method = “none”)

I have a dataset with 1205 observations and 285 predictors (all but one categorical). It is a binary classification task. When I run randomForest, it executes in less than 1 second. When I run ...
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Does Random Forest ever compare the splitting of one node to the slitting of a **different** node?

I thought I understood how a single decision tree is constructed as part of a Random Forest : The data is split recursively until some kind of stopping conditions are met. Each split is ...
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Why does a regression tree not split based on variance?

When choosing each split, recursively, in a regression tree, I understand that you want to measure the spread, in each side of the split, essentially. So, in some sources, including this one at 6 ...
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126 views

XG Boost vs Random Forest for Time Series Regression Forecasting

I am using R's implementation of XGboost and Random forest to generate 1-day ahead forecasts for revenue. I have about 200 rows and 50 predictors. (As I go further in time I have more data so more ...
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50 views

Accuracy of training sample in Random Forest model in R

I'm using a Random Forest algorithm in order to construct a classification model, and I HAVE to check the accuracy of my rf model in the training sample, but as you can see in this answers : ...
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Confidence Score for Unsupervised randomForest?

I am using the unsupervised form of randomForest (R) as part of a clustering scheme for 1000 peaks based on 30 features. I use the proximity matrix produced by ...
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234 views

Interpretation of results using caret R package and random forests regarding training a classifier

In conjuction to one of my previous posts, (Important questions regarding the methodology for constructing classifiers with R package caret and tree based algorithms) i used the R package caret and ...
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Important questions regarding the methodology for constructing classifiers with R package caret and tree based algorithms

I'm currently playing with caret R package, with one merged microarray affymetrix dataset(paired tissue samples), in order to build and test various classifiers, mostly based on trees-such as random ...
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Can random forests use cross validation?

As cross validation may help limit overfitting, I think this tech can also help random forest to avoid overfitting sometime. But it is little weird using cross validation because random forests are ...
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Choosing Random Forests' parameters

I am new to machine learning and would like to know if it makes sense to fix the number of estimators and the maximal depth of a random forest with cross validation ? My intuition would be that yes, ...
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45 views

Using probabilities as predictor variables for binary classification

I have training data with each feature being different sources of probability. All of the features are probabilities (between 0 and 1 obviously). This is a binary classification problem. Note ...
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25 views

random forest case sampling strategy for model with maximum positive predictive value

I want to create a binary classifier that gets as many true positives with the lowest possible false positive rate. False negatives and true negatives dont matter, just the false positive rate has to ...
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64 views

how does multicollinearity affect feature importances in random forest classifier?

I have a random forest binary classifier, but the results from the feature importances are somewhat erratic. Here's what I want to know: Does multicollinearity ...
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32 views

cforest - Prediction without labels

I have some data that I want to get the important variables from. I want to use random forest to get this information. The problem is that the data does not have labels. From what I understand random ...
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28 views

Positive and negative impact of predictors on responses in data mining models

My question is an extension to the question asked here. How does one identify the parity of predictor/feature/variable impact on response/outcome in a data mining model. Is there a standard procedure ...
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R - Applying cforest to a dataset with no outcomes

I want to build a random forest model using a genomic dataset that has 1000+ columns and 20 samples and has no outcomes. I decided to generate an outcomes column with random true and false values. I ...
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Use of Random Forests for variable importance as preprocess before another analysis

the question Demonstrate the speed and accuracy of properly applied 'Random Forest' as a variable importance selection tool especially in handling very large data against alternative approaches such ...