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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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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14 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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1answer
27 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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11 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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24 views

What is the difference between two implementations of a random forest? [closed]

Question 1: What is the difference between these two implementations of a random forest? Both models RF1 and RF2 use repeated cross validation. The only difference I see is that the number of trees is ...
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1answer
23 views

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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13 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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8 views

Differences between method=“parRF” and method=“rf” [migrated]

Want to optimize computation time for random forests and the caret package has a built-in train function that allows running parallel random forests. I'm new to the caret package, so don't understand ...
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2answers
43 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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random forest with certain variables always being selected

So I have one variable I think is very important for decision tree. Is there a way I can make sure it is always being selected in each tree of the random forest? I have checked R and Python related ...
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10 views

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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1answer
28 views

Can you add the probabilities of a classifier to better predict an outcome?

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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1answer
63 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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1answer
39 views

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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1answer
50 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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37 views

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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21 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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18 views

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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1answer
47 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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2 views

Error in train.default(x, y, weights = w, …) : final tuning parameters could not be determined [migrated]

I am very new at machine learning and am attempting the forest cover prediction competition on Kaggle, but I am getting hung up pretty early on. I get the following error when I run the code below. ...
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35 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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1answer
50 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 ...
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1answer
87 views

Multiple regression or anova or bestglm or forestplot or Boruta

I have data on a continuous health variable and following others: age, gender, height, weight, waist, city and season. I applied multiple regression and got following output: (age, gender, height, ...
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32 views

variable reduction before doing random forest in R

I have a dataset featuring around 50 predictors, some of which are correlated. Now I am trying to fit a random forest model in R for prediction purpose with this dataset. Because there are too many ...
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38 views

Random Forest Probabilistic Prediction vs majority vote

Scikit learn seems to use probabilistic prediction instead of majority vote for the model aggregation technique without an explanation as to why (1.9.2.1. Random Forests). Is there a clear ...
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43 views

Explanation for large difference in SVM and Naive bayes results

I have a dataset with 389 data evenly distributed into 6 classes. Each data has 1024 features. So my dimension is much larger than my observation data. I have tried to see some common classifiers on ...
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1answer
40 views

Which performance measure to report?

I've trained a random forest regression model using boot632 resampling and the caret package. The output of the model tuning process gives a few different performance measures. ...
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2answers
81 views

SVM heavily over fits the data (classifying Highly Unbalanced data )

I have a huge training set from which I am supposed to regress and classify, i.e I classify whether an event will occur or not and another task is to regress the intensity of the event in future. The ...
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14 views

Binary classifier issues

I am trying to predict if sales are going up or done given a specific set of features. The only thing I care about is precision here. In this context I tried a few classifiers ( SVM, Random Forest ) ...
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1answer
39 views

Visualize difference between 2 classifiers

I trained 2 binary classifiers with the same data (a Decision Tree and a Random Forest). They both made a prediction on the same test data. Now, I want to visualize the difference in classification ...
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22 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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1answer
41 views

If random forests gives me a bad cross-validation score, should I trust it for feature selection?

I get an R^2 value of about 0.22 when I 10-fold cross-validate with my entire dataset. My main use for random forests is to analyze feature interactions. But should I trust the feature importances ...
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1answer
32 views

Random Forest Algorithm Where to start?

I need to solve a real time classification problem using random forest algorithm.Can anyone suggest any book,website where I should start looking it ?
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20 views

fragmentation problem in decision tree

I am taking a NLP class, in which it says decision tree has the fragmentation problem. It says ...
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1answer
15 views

For the given type of dataset, what would generally be the set of classifiers that should be tried to get the highest TPR for FPR = 0.01

I'm primarily looking to attain the maximum True Positive Rate for a small False positive Rate of say 0.01. The following is an instance: 1 37.33 228.39 0 77.060599 0.073384 0.052536 ...
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25 views

How is the best split point determined/predictor calculated in a regression random forest?

In a regression tree (I am particularly interested in random forest regression, but it seems like this can be generalised to regression trees as a whole) a number of random variables is selected at a ...
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1answer
81 views

Classification score for Random Forest

I'm learning about the Decision Tree and Random Forests. But there is something I don't really understand. I have a training set and a cross-validation set. I need to train different Random Forests, ...
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27 views

Important Variable Direction in Random Forests

In a logistic regression a positive/negative beta tells you that the direction that variable works. Is there anything in the Random Forest variable importance measures that indicates the direction of ...
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22 views

Python “feature_importances” for most important factors

I'm a little unsure as whether this belongs in stackoverflow or cross validated. I have found a few posts on this topic , but I have not found the following question. Is it accurate to run the feature ...
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1answer
24 views

Analysis of Feature Importances when features are dependent on one another

I can use random forests to determine which features are important when doing a prediction problem; for example. < height, weight, IQ measure> -> Is considered obese? Applying random forests ...
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24 views

How proximity and multidimensional scaling relate? #random forest

I have downloaded randomForest package of Breiman and try to use function MDsplot to plot the proximity of the data like the example in the manual ...
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61 views

Package ‘randomForest’ R defining variable importance in advance

I am planning to build 5 successive random forests (RF) on a same data using r 'randomForest' package. I am leveraging work done as per the page. while building the first RF, each X variable should ...
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38 views

regarding using Lasso and Random forest based on the variable selection result coming from other processes

After the process of data exploration process and discussion with client, we set up a set of variables as follows: T1, T2, T3, T6, T8, T2*T3, T1*t6 During ...
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55 views

Using an RMSE with derived confidence interval, to generate a prediction interval for an estimate

Previous questions have asked about creating prediction intervals for estimates derived from random forests or boosted regression trees, in a similar way to is easily achieved with linear regression ...
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19 views

input variables with different order of magnitude [duplicate]

I need to build a prediction model based on a data set with 5 different independent variables. The data set looks like as follows. The variables in col4 and ...
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1answer
59 views

Regarding the different variable selection result between regression modeling and random forest

I build a prediction modeling using both regression and random forest. ...
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74 views

Random Forest Regressor - Incorporating Sample Weights in scikit-learn

I am running randomforestregressor in python. The target variable I am modeling is the frequency of an event occurring per unit of time. Each record in my data includes whether or not the event ...
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1answer
61 views

Using random forest for survival analysis with time varying covariates

I've been trying to train a model that predicts an individual's survival time. My training set is an unbalanced panel; it has multiple observations per individual and thus time varying covariates. ...
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33 views

Is 100% accuracy using randomForest indicative of anything wrong?

I am getting a 100% accurate result on randomForest model in R for loan default data even when my training set and test set are completely non-overlapping. I am using abt 8 parameters/features for ...