Stands for 'Classification And Regression Trees'. CART is a technique for developing a tree model (T) to predict categories (C) and/or continuous values (R) by recursive partitioning. It does not make restrictive parametric assumptions. CART is a popular data mining technique.

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17 views

How to further reduce predictive error in a regression tree model

I had fitted 480 records to a regression tree model, and validated it with a validation data set of 120 records ( I used a 80/20 split ), I calculated the predictive error of MSE to be about 5.6037. ...
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11 views

Complex event processing

I work in an M2M engineering startup and the engineering team have been conceptualizing a complex event processor and want to build "alerts" when an event might occur. The initial plan was to build a ...
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2answers
68 views

odds ratio from decision tree and random forest

I am using a decision tree and random forest for a classification problem. The output is binary {0,1} and some of the input variables are categorical while the others are continuous. I would like ...
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0answers
93 views

Random forest on multi-level/hierarchical-structured data

I am quite new to machine learning, CART-techniques and the like, and I hope my naivete isn't too obvious. How does Random Forest handle multi-level/hierarchical data structures (for example when ...
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0answers
11 views

What is the common terminology to refer to the nth ancestor of a tree root? [migrated]

Reading the Wikipedia article for common terminology for tree (data structure) there are several near references, but I don't read a formal declaration for how to refer to a specific generation of a ...
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0answers
35 views

ML algorithm to find optimal control parameter

I have a training dataset $(X, y) \rightarrow z$. Where $X$ is an $n$th dimensional real vector, $y$ is an integer number in $\{1, 2, 3\}$, and $z$ is a real number. I am looking for machine learning ...
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0answers
15 views

How to use gain and splitinfo for continuous attributes? [duplicate]

I am implementing a C4.5 algorithm, and for that I need to perform attribute selection. How do I use the gain and split info equation to find the best attribute?
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0answers
12 views

Decision Trees - Test & Training Data Difference

In decision tree modelling what is the difference between Training & Test Data ? i am having some difficulty in locating the meaning of these two words and i hope you guys can help me out. Could ...
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1answer
35 views

Regression Tree when target is a ratio

I am learning a regression tree for data of the form $(x_i,y_i)$: $x_i = (1, 0, 1, ...., 1 , 1)$ a multiple input vector and $y$ is a ratio of the number of observations divided by the number of ...
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28 views

Advice on how to analyse “customer-data” in R

consider the following example data: ...
2
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0answers
50 views

Analyzing CART type trees in R--options for grouping results, formatting plots

I'm new to using CART trees, but have been asked to do so for a project I'm working on. I've had success running the scripts (from both RPART and PARTY packages) but I can't seem to get exactly what ...
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1answer
34 views

In decision tree construction, can a good splitter have low information gain?

I have a data set with a candidate splitter variable that is a natural choice from the business perspective. It has two values, and the distributions of the target when conditioned on the two values ...
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14 views

Choosing the values of a proper subset of features to maximise regression tree output

Suppose I have a regression tree and feature set $X$. Suppose that the feature set is composed of $X:=\{X_0,X_1,...,X_{100}\}$, where each $X_i \sim N(0,\sigma^2)$. Suppose that ...
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1answer
40 views

What methods exist for finding optimal splits to discretize continuous data with respect to a target variable

I'm doing some research into methods for discretizing a continuous variable coupled with a binary target variable to find the optimal split points to maxamise a measure of impurity (gini/entropy). ...
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0answers
7 views

Feature importance and interpretation of alternating decision trees

Is there a way of calculating feature importance in alternating decision trees? What if I've already trained an alternating decision tree and want to calculate feature importance in terms of ...
2
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0answers
23 views

Multi-output decision tree

I have a dataset of 1000 tumours described by 6 parameters (my independent variables). For each tumour I have a value of the accuracy of 8 different segmentation methods. I would like to build a ...
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0answers
13 views

Standardizing inputs for CART

I know I do not need to standardize the predictor variables before applying CART but would there be any adverse effects to doing it anyway? I'm comparing CART to a linear regression where I did ...
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1answer
29 views

Tree analysis - CHAID

I am running a decision tree analysis, and the same predictor that forms the first core branch, reappears as an another branch further down the tree. Could somebody explain me how this is possible? ...
3
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1answer
42 views

Why the trees generated via bagging are identically distributed?

I have problem in intuitive understanding of following arguement: "The trees generated via bagging are identically distributed, thus the expectation of the average of a set of trees is the same as ...
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1answer
55 views

Validating the CART model in R

I have built the CART model, however I want to understand how we predict/validate the results with Validation data. ...
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0answers
48 views

Binary Class Distribution Effects on Probability Scores - (gbm) Boosted Tree Regression Models

Any help would be greatly appreciated. Problem: I need help to better understand the probability scores that come from the result of a decision tree model. Specifically, I'm using the gbm package ...
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0answers
51 views

Random forest performs worse than single CART tree?

I have a data sample of ~5000 observations, ~700 predictors and 2 classes. I've built a classification model based on RF with 500 trees using randomForest R library. Than I've estimated the ...
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0answers
46 views

Tricks for a very fast implementation of Random Forest

I am implementing my own Random (regression) Forest algorithm and am looking for tricks to speed up the estimation of forests on large datasets. So far I have implemented three main tricks: 1) Use a ...
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0answers
20 views

Finding range of time with best probability of positive event occurring

I have data representing a couple hundred of independent experiments. Each one contains time - how long did the experiment took and outcome: positive and negative. There is 10% of positive outcomes. ...
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0answers
20 views

Random classification forests for extremely sparse response variables

I have a response variable that can be $A,B,C$. It is very sparse, meaning 99% of the sample is $B$ and the rest is approximately evenly divided between $A$ and $C$. How do I predict this variable in ...
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0answers
16 views

Early split decision criteria for fast random (regression) forest estimation

Suppose I am on a node in a $regression$ tree and I am using running estimates of $\sum_{i \in Region_1} (y_i - mean(y_i)_{Region1})^2$ (and the same for Region 2) to determine whether to split the ...
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0answers
13 views

How to get more continuity in regression forest output

I am using a regression forest. What I have noticed when I plot the quantile distribution of the forest's output is that over a long stretch of quantiles (e.g. $\tau \in [0.1,0.3]$), the output will ...
3
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0answers
37 views

Standard deviation in regression trees

In a regression tree, it is often assumed that each leaf is a Gaussian distribution $\mathcal{N}(\mu_i, \sigma)$, where $i$ is the index the leaf. Is $\sigma$ calculated as the standard deviation ...
3
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1answer
49 views

How to split nodes in regression trees

I am looking for a comparison of different regression tree node splitting approaches within the random forest framework. I am looking at the trade-off between ensemble accuracy/reliability (holding ...
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0answers
16 views

Discretization of the split in random forest estimation

If the dataset I'm working with has too many independent variable dimensions, I may need to choose a proper subset of all possible locations in my feature vector to determine whether I am going to use ...
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1answer
65 views

How to incorporate constraints in random forest output

Suppose I am doing random forest classification of labels $A$,$B$,$C$,$D$. There is some theoretical ordering to this output such that when $A$ is more likely than $B$, $B$ is also more likely than ...
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1answer
47 views

Can we remove trees from a random forest with poor OOB error to improve generalisation?

My objective is to improve out of sample generalization of my random forest while holding the number of trees constant. Suppose that I am only allowed to use $n$ trees on the out of sample data but ...
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0answers
14 views

Decision trees: nominal values unseen at training time

What is the best approach to deal with unexpected nominal values, unseen by decision trees at training time?
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0answers
44 views

Minimum sample size for CART

What would be the minimum sample size for a CART analysis? I've found this recommendation: The original CART monograph discusses a study the authors performed working with 215 observations and ...
3
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1answer
42 views

Decision tree for numeric dependent variable?

I have data on commute times over a specified route over different days during different conditions. Some of the conditions are categorical (e.g., weather, traffic), and some of them are numeric ...
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1answer
43 views

Using regression forest for a factor variable

I want to predict if a customer is interested in a new product and I use the randomForest package for that. Target variable : factor (Yes or No) so I use the randomForest for classification : ...
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1answer
83 views

How to interpret scikit learn classification tree?

I'm currently trying to work with scikit-learn classification tree. I followed the example on iris dataset : http://scikit-learn.org/stable/modules/tree.html and everything is working fine. I do ...
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1answer
85 views

Random forests visual introduction-level reference or tutorial

I have seen several references, but am looking for something easy to follow that illustrates Random Forests in regression and feature importance applications. I want to make sure that I explain this ...
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1answer
46 views

Regression tree model splitting too far - random data column?

I am attempting to make some regression trees with many potential independent variables which comprise both categorical and continuous data types of widely varying scales i have been using a few ...
1
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1answer
87 views

Theoretical error bounds of classification and regression trees

So, some algorithms were motivated by theoretical work, such as in the case of boosting. Adaboost was introduced as an algorithm for solving the hypothesis boosting problem. The bounds on the training ...
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2answers
85 views

The first principal component becomes irrelevant

I did run PCA on 17 quantitative variables in order to obtain a smaller set of variables that is principal components to be used in supervised machine learning for classifying instances into two ...
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0answers
56 views

Classification Tree Analysis - Assessment of tree explanatory power (R-square?) using the party package in R

I have produced a model using the ctree function in R, and want to know whether this tree is actually explaining my data well. I am trying to explain the presence ...
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0answers
21 views

how to calculate information value [duplicate]

i have the following tree Outlook( [Sunny] => (Yes, Yes, No, No, No), [Overcast] => (Yes, Yes, Yes, Yes), [Rainy] => (Yes, Yes, Yes, No, No) ) ...
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1answer
76 views

Random Forest variable importance metric for predicted value

Let's say I'm using a random forest in a true/false classification problem. When I produce a score for an observation is it possible to get some sort of metric of variable importance for that ...
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2answers
92 views

How Gradient boosting can be more interpretable than CART?

I found this document which compare some learning methods and I don't understand this table : Gradient boosting has a better intepratability score than CART. How is it possible ? I thought gradient ...
2
votes
2answers
154 views

Regression tree algorithm with linear regression models in each leaf

Short version: I'm looking for an R package that can build decision trees whereas each leaf in the decision tree is a full Linear Regression model. AFAIK, the library ...
0
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1answer
52 views

R mvpart - splitting index [closed]

I need to understand what mvpart is doing. Which index does it use as a splitting criterion (in my case, method=class)? Does it use simultaneous partitioning along ...
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0answers
52 views

Statistical tools to be used for small data sets

I am a newbie to statistics. I am trying to model admission criteria for B-School based on one past years data. It has roughly 370 samples. I am trying to set admission criteria taking their ...
4
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1answer
72 views

Should pruning be avoided for bagging (with decision trees)?

I came by several posts and papers claiming that pruning trees in a "bagging" ensemble of trees is not needed (see 1). However, is it necessarily (or at least in some known cases) damaging to perform ...
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23 views

Rule based pruning clarification

I'm trying to answer a homework question and have found the following example online similar to what I need to accomplish for classifying unknown data in a decision tree. I don't quite understand why ...