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Questions tagged [cart]

'Classification And Regression Trees'. CART is a popular data mining technique.

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In Gradient Boosting Tree, why do we fit the tree on the residuals and not on the sum of the previous function and the residuals?

In the Gradient Boosting Tree algorithm, as described in https://en.wikipedia.org/wiki/Gradient_boosting#Gradient_tree_boosting, we update the previous model $F_m$ by adding the results $h_m$ of the ...
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1answer
88 views

Please correct my assumption on how regression trees work

I'm trying to understand how regression trees work, I've been experimenting with catboost and xgboost in python, and I'm getting results which I don't expect, can someone please clarify (and apologies ...
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2answers
53 views

Why are decision trees considered supervised learning?

It seems to work similar to clustering algorithms, where data does not have to be labeled, and the algorithm creates it's own labels/groups based on feature similarities...
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11 views

How to reduce the number of rules in decision tree with Support and Confidence

The following is a set of rules in decison tree. How to reduce the number of rules in the set with Support and Confidence? If Ascites = 'Yes' then if Class = 'Live' then if Spiders = 'Yes' then if ...
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10 views

Rpart and subsets

I am getting unexpected results from an rpart model, where the model selects two variables, one of which is a subset of the other. This in itself is not unexpected, but the seemingly odd thing is that ...
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1answer
28 views

How do I use a random forest regression once trained [closed]

Similar questions have been asked on these two posts: random forest how to use the results and How to use random forest for regression after it is trained but I feel like the replies didn't give ...
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16 views

How to prevent GBDT from splitting on uninformative features?

I'm looking into using feature importance scores from GBDT for feature selection. Although GBDT does not need manual feature selection, the number of features is a restriction of the production system ...
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0answers
29 views

Model selection using p-values - tree inference

Suppose I have some i.i.d. normal observations from $\mathbb{R}^f$ with parameters $(\mu, \Sigma)$ and $\Sigma$ is known to be the identity matrix. I have the following hypotheses: $H_0^i$: $\mu_i = ...
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4 views

Few Queries regarding CART and logistic algorithms and missing data

I have few queries regarding ML algorithms and data, It would be great if you can provide some feedback on that. Which is best package to impute the missing data (currently using MICE in R) and how ...
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1answer
96 views

Using a decision tree to predict a relevant location to a user

I am trying to create a decision tree to predict new locations a user would like to visit based on previous locations they have liked. Here is my problem, I have two data sets, a user data set ...
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0answers
42 views

Theoretical bound on the uncertainty provided by decision trees?

Decision trees have the property that they provide both a prediction and a probability for this prediction (scikit-learn's predict_proba method; Section 3.4 of Data ...
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0answers
15 views

Why do RMSE values increase on a smaller tree (RPART)

AIM: I want to understand why does RMSE increase on a smaller tree. CONTEXT: I am learning the rpart algorithm. I had some data,...
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10 views

Using cosine of measurement time as feature for decision trees VS NNs

I have a regression data set and I'm trying to do some feature engineering. The data set is foot fall coming into a store measured on the hour. I'd like to include the time of measurement as a ...
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14 views

How to do a right model selection for modeling decision trees in R

I struggle with modeling a decision tree. I am trying to model both types (classification and regression), but let's stay for the first with regression tree. I do have a large data set of 200000 ...
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4answers
2k views

Random Forest and Decision Tree Algorithm

A random forest is a collection of decision trees following the bagging concept. When we move from one decision tree to the next decision tree then how does the information learned by last decision ...
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0answers
7 views

How to extract and filter rules from C5.0 package for making fuzzy rules

I have project on a ML dataset. I have performed decision tree using C5.0 package in R. For the next part I want to make fuzzy logic based on C5.0 results. I have 2 questions: 1- How can I filter and ...
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0answers
28 views

Interpreting printcp for classification trees in R

I'm trying to fit a classification tree model to the credit card fraud data from kaggle using rpart in R. Once I come up with a model and call printcp(), I get the following output: ...
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0answers
5 views

Method for predicting whether a change in survey presentation had significant effect on survey response

I am not experienced in qualitative research in general and so I am hoping to get some guidance on this particular question I am dealing with (and perhaps from there I can extrapolate to some more ...
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5 views

What does the attribute splitter in DecisionTreeClassifier do?

The splitter attribute in the constructor of DecisionTreeClassifier(or DecisionTreeRegressor) can take two values 'best' or 'random'. What does the attribute splitter in DecisionTreeClassifier do?
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1answer
59 views

Gradient Boosted Trees vs Neural Network for limited data [closed]

I have a classification problem, with about 10 different inputs, some boolean, some categorical (and unrelated to each other), some being a float between 0 and 1, which need to be mapped to 4 ...
2
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1answer
64 views

Regression tree with nested data repeated in time (GLMERTREE, REEMTREE or REEMCTREE)

I work on the predation of seeds by insects (carabidae), and I am particularly interested in the effect of community composition on predation. I would like to know if the best predation rates are ...
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0answers
24 views

Boosted decision trees: in which situations are “deep” decision trees performing better?

The general idea of boosted decision trees is to use very simple trees in the following manner (simplified, for intuition only): start with a simple tree, fit another simple tree on the residuals, ...
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33 views

Penalty matrix on Decision Tree

I am doing a project on predicting credit risk and am trying to use decision trees. First, I trained the tree without any penalty matrix. Next, I defined my penalty matrix as $$ \begin{matrix} 0 &...
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0answers
8 views

Data synthesis approach for sample sample prediction

I have to forecast sales of a temporal SKU which only sold one month in a year. I have sales figures for nearly 1000 different sales locations. Moreover, my data is limited for only 3 years. So I ...
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0answers
15 views

Will a regression tree work for grouping combinations of variables?

I would like to discover patterns in my dataset. For example, Var1 var2 var3 var4 var5 var6 1 0 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 I have decided to use ...
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2answers
37 views

Depth of Decision Tree

If there are only categorical variables in the dataset, will the depth of the decision tree be equal to the number of attributes? If not, can a value be split again?
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0answers
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Is it possible to use Information Gain metric for CART?

I've been looking for an example of CART using Information Gain but haven't found one. This made me wonder if it was even possible, so I tried to train one manually (by hand) using the dataset below ...
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1answer
20 views

DECISION TREE : How to calculated for repeat decision noded such as this picture (C5.0 Algorithm -Decision tree)

I confused about decision tree such as this picture why repeat decision node.Could you please explain that decision tree. thank you
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2answers
29 views

Decision Tree in layman terms

How can we describe decision tree in laymen's language and what are the major fields that require this?
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1answer
25 views

Question when using sklearn's DecisionTreeClassifier

sklearn's DecisionTreeClassifier is not behaving as I expected. From the following: ...
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0answers
25 views

Hypothesis space of regression/classification tree

I'm wondering how to exactly define the hypothesis space of a tree. For simplicity start with a regression tree $f(x) = \sum_{m=1}^Mc_m\mathbb{I}(x \in R_m)$ Where $c_m$ are the ( $M$ ) constant ...
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1answer
36 views

`randomForest` predict() for continuous variable: unexpected output?

I am trying to understand how predict() in randomForest() in R computes the predicted values for a continuous y? My ...
10
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1answer
294 views

Boosting AND Bagging Trees (XGBoost, LightGBM)

There are many blog posts, YouTube videos, etc. about the ideas of bagging or boosting trees. My general understanding is that the pseudo code for each is: Bagging: Take N random samples of x% of ...
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1answer
19 views

How to categorize data as others if training set is not available?

I run into a problem. I am using the decision tree to classify the incident category based on the short description the user has used while logging the ticket. I have the training data only for 5 ...
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0answers
13 views

Why is the sqrt(n_features) the default maximum number of features for the best split in RandomForestClassifier? [duplicate]

Why does sklearn.ensemble.RandomForestClassifier references have $\sqrt{n}$ in the max_features implementation and why does randomForest in R seem to have the same $\sqrt{n}$ default? I am looking for ...
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2answers
66 views

multicollinearity resulting in high variance

Section 8.7.1 of Elements of Statistical Learning talks about high variance in a classification tree due to high correlation between features. What is the intuition behind this? I would think that ...
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0answers
18 views

How can I run a decision tree algorithm with a specific hierarchy of variables and with many missing values?

I asked students in learning groups what their biggest learning problem was "today" for each learner. The biggest problem could either be "motivational" (=motivation problem) or cognitive (="knowledge ...
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1answer
38 views

Classification Tree or Regression Tree?

I have time series data: students that learned in groups for minimum 3 times and maximum 10 times and for each learning group session had to state if they faced a motivational OR a cognitive problem, ...
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1answer
122 views

optimal decision tree np-hard

Reading Elements of Statistical Learning and it says that decision trees are often constructed using greedy algorithms because it is computationally infeasible to create an optimal decision tree. ...
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1answer
49 views

Decision tree without the “tree”

I would like to construct something like a decision tree. However, instead of using "recursive partitioning" to build a tree, I would like to find an optimal set of "global" splits. For example, in a ...
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1answer
24 views

Merge one label with one information for classification problem or multi-label classification

I want to build a model to support decision making in order to propose or not loan insurance to clients. Because sometimes clients asking loan and loan insurance have less chance to have their loan ...
2
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1answer
19 views

what if a decision tree does not result in leaves with one class each?

A decision tree can result in leaf nodes that have samples from multiple classes. Is the algorithm at that point to simply vote on the class?
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10 views

Is pseudo-r2 enough to validate a CART?

I run some Classification and Regression Trees (CARTs) and computed the pseudo-$R^2$ from McFadden. Is that enough to validate the trees or do I need some other test to be sure there is no overfitting?...
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0answers
53 views

Difference between Random Forest and Random Subspaces/Patches

When fitting a Random Forest model, a subset of the features is randomly considered at the splitting of each node. E.g., if $p$ is the number of features, then at each node in each tree, $\sqrt{p}$ ...
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0answers
33 views

Which algorithms for mixed type datasets (binary classification)?

I am new to machine learning and I am trying to implement a model for a binary classification problem (output class 0 or class 1), and wondering which algorithms I should consider, since my dataset is ...
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0answers
33 views

What is the relation between minimum instances per node and max depth?

In bagging and boosting models like random forest and xgboost we have hyper-parameters like minimum instances per node and max depth. If max depth is high the minimum instances per node will be less ...
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1answer
51 views

Which model for feature importances?

When wanting to find which features are the most important in a dataset, most people use a linear model - in most cases an L1 regularized one (i.e. Lasso). However, tree based algorithms have their ...
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0answers
22 views

True or False? Not testing data is needed for CART if there is not future prediction

I am analysing a data set with a relatively a small sample size. For the nature of my data, it is considered already quite large, but for its statistical power it is just in the lower limit. I sampled ...
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0answers
12 views

Small sample size for partitioning and evaluating a CART

I have a data set of 130 samples. If I partition the data 70/30 % to run a conditional tree, CART, and then evaluate it, the results are different than if I run the 100% of my data in the CART. When I ...
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0answers
43 views

Decision Tree with unbalanced dataset in SAS

I have a dataset with a binary target variable. This variable is highly imbalanced i.e. the # of True case is ~1% and # of False cases is ~99% The other limitation I have is that I can only use ...