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Questions tagged [cart]

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

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XGBoost tree dump contains lots of empty trees

After fitting a regression model using XGBoost, I want to inspect the individual trees that were built. In the resulting table, I find a lot of 0-depth trees, i.e. trees with only a leaf node, and ...
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1answer
32 views

Solve the optimization problem of tree, should we make each rectangle contains exactly one training data point?

I was reading the book "An Introduction to Statistical Learning with Applications in R". In page 306, when talking about the objective function of tree model, the book says: "The goal is to find ...
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12 views

Difference between C5.0 and a 'regular' decision tree

After reading some descriptions of C5.0 trees, it appears to me that it is the same as using e.g. sklearn DecisionTreeClassifier with ...
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20 views

Using Regression Trees for Univariate Time Series Data

I have a monthly time series (105 observations) including trend and seasonality and want to forecast the numeric values. I initially tested with the Box-Jenkins approach and other univariate models ...
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38 views

“Hierarchical” Random forests?

Background I am using Random Forest to classify ~900 objects based on a large number (> 80) predictors. I split these 70:30 for training and testing. The overall model does fairly well, giving an ...
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1answer
68 views

how does splitting at a node occur in a decision-tree with non-categorical data?

According to a website (:http://dataaspirant.com/2017/01/30/how-decision-tree-algorithm-works/) , these values are chosen randomly: I don't think this is the case with any optimized solution of a ...
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2answers
45 views

Why are all predictions made by XGBoost distinct?

If I understood correctly the XGBoost is a framework that operates on gradient tree boosting. It means that behind the scenes, it uses a decision tree to make a prediction. So, from what I read in the ...
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1answer
16 views

How to estimate the leafsize of the kd-tree?

The kd-tree implementation proposed by the scipy python libray asks for the value of the leafsize parameter that is to say the maximum number of points a node can hold. It is by default set to 10. ...
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30 views

Xgboost / Boosted decision trees: Representing categorical id numbers as continuous integer variable

I've been reading through some kernels at kaggle.com for a sales forecasting competition, and noticed that a lot of people using Xgboost are feeding it categorial ID variables, represented as ...
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27 views

Understanding a Classification Tree applied to the iris dataset

I used the iris dataset for a simple regression tree to see how things work. I am generally interested in two aspects: Why does the tree not use the Sepal data to improve the classification What do ...
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0answers
22 views

Difference between tree package and manual computation

I have a simple example (only one independent variable) to do the regression tree using the tree package in R, which reads ...
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1answer
13 views

Decision Tree on a set with reliabilty information

I've got an introductory AI course in my university, and I was taught about decision trees. I'm now facing a classification problem that seems solvable with a DT, but I'm stuck with an unseen ...
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1answer
26 views

How to engineer a bimodal continuous feature for use in Decision Tree?

I have a predictor that exhibits "bimodal" behaviour. How can I engineer this feature to improve performance within a Decision Tree? For an intuitive example, consider how a binary flag of "moves ...
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28 views

Quantifying uncertainty of predictions for new data in the regression tree

I used Regression Learner to train my data. I held out 25% of the input for validation and ran different models for training. Based on the results using RMSE and R-squared, I decided to go for the ...
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0answers
30 views

Statistical conclusions based on conditional trees

I have a complex dataset, number of features is much bigger than number of samples. The question is - which features are important for classification into 2 groups. I think that (after some ...
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1answer
27 views

default plot for mob object (glm tree) not returned; using party package [closed]

I'm trying to plot a glm tree using the package party. Per the reference guide, the default plot of the terminal node should be a spinogram as in this image but I ...
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0answers
19 views

Classification trees: contingency table and Gini index at split point (grouped categories)

I am working through chapter 14 of the book Applied predictive modeling (Kuhn, Max, and Kjell Johnson. Applied predictive modeling. Vol. 26. New York: Springer, 2013.). For tree models with categorial ...
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8 views

Usage of correlated (non-linear correlation) variables in an experiment and standardisation of variables values

The setting (see the dataset at the end of the question) The setting of the problem is this: I ask many multiple choice questions; only 1 out of the 3 available answers is correct; I ask the same ...
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1answer
33 views

Repeating CV multiple times

I understood the purpose behind CV (i.e. just to make sure the data is spread well across the folds, and the skewness is somewhat averaged-out). Let's take this example - ...
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52 views

How to interpret feature importance in a decision tree after applying Factor Analysis

I'm using SKlearn to apply Factor Analysis (FA) to my data before training a Decision Tree. I then want to do an importance analysis. If I had not applied FA to my data, I could just call clf....
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14 views

SHAP values vs Information Gain?

SHAP values which are essentially the variable importance at a local level where each variable's importance is assessed by different in probability outcome of a model with and without the variable. I ...
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1answer
14 views

Shrink decision tree by shuffling order of attributes

I made a decision tree that classifies mushrooms in the UCI Mushroom dataset as either poisonous or edible based on their features. The model has a 100% accuracy on both the training and test set. ...
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32 views

ELI5: The logic behind tree building

I'm studying trees for the first time and I got stuck on a theoretical passage: on ISLR (Hastie, James, Tibshirani, Witten) I found that considering that: $$\sum_{j=1}^{J} \sum_{i\in Rj} (y-\hat{y}_{...
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1answer
14 views

How splits are calculated in Decision tree regression in python

I'm using scikit-learn to build a decision tree (or a random forrest) for a regression problem. I have continuous variables as my regressors. I wonder to know how the splits in a regression decision ...
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2answers
666 views

Why log-transform to normal distribution for decision trees?

On page 304 of chapter 8 of An Introduction to Statistical Learning with Applications in R (James et al.), the authors say: We use the Hitters data set to predict a baseball player’s Salary based ...
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6 views

CART model: Performance differences depending on preprocessing

I am trying to fit a CART model on a spam dataset, trying to predict "spam" or "ham". Usually, I work with the "tidy" packages for preprocessing; however, I now learnt about another way in a tutorial,...
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2answers
53 views

When MSE for CV is greater than test MSE?

In an introduction to statistical learning book, on page 311 there is a figure which compares MSE vs the number of leaves. I like to know the reason that Cross-Validation's Mean Square Error curve is ...
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2answers
42 views

Is some degree of overfitting always going to occur in tree based models?

So, I am somewhat new to machine learning, and I am trying my hand at a bunch of different Kaggle datasets. In a lot of the datasets that I ended up a tree-based model on, I noticed one that all of ...
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0answers
7 views

How to determine whether pruning of decision tree is necessary?

I am really new to decision trees. I constructed classification tree (estimate certification using grade and assignment) and calculated cp: ...
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0answers
9 views

Evaluating multiclass decision tree as a probability function

I have a problem where I am trying to estimate the transition probability within a state-diagram. For example, estimating the chances of transitioning from a happy mood to one of 3 possibilities (...
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0answers
26 views

Incremental/Online Learning for Reinforcement Learning

I have been reading the book Reinforcement Learning: An Introduction by Sutton and Barto, and I have some questions about extending the value function approximation to non-differential functions: Is ...
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1answer
36 views

catboost ignoring my holiday category

I'm experimenting with catboost, predicting electricity demand from temperature, timeofday, dayofweek and if a day is a public holiday or not (and a few other continuous and categorical columns but ...
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0answers
23 views

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
104 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
81 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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0answers
14 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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0answers
32 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
31 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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27 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
34 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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1answer
110 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
25 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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1answer
22 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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5answers
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
9 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
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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0answers
9 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
145 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 ...
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1answer
99 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
26 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, ...