Questions tagged [decision-trees]

A decision tree is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm.

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13 views

Which models implicitly consider interaction between features?

I would like to understand more how different models (NN and RF specifically, but any other as well) consider interaction between features in tabular data? For example, can the model figure out while ...
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Tree Regression

I am learning about classification and regression trees. Can anyone suggest a reference for the asymptotic theory for tree classification and regression? All I can find seems to be about ensemble ...
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Does converting continuous variable to discrete(categorical) variable increases accuracy of a tree based model?

I've read other questions regarding if a continuous feature should be converted to categorical or not. But I'm interested in case of tree based classifiers such as Decision Tree, Random Forest, ...
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What is the difference between a decision tree and something called “subgroup discovery algorithms”?

I'm reading a paper which states that subgroup discovery is: ...
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What is the best possible method/methods to determine best possible branch(rule) in a decision tree plot for the positive cases only?

I have a dataset, which I am using for loan prediction. Thus, it is pretty much clear that my dataset is imbalanced. I have used Decision Tree to plot the tree structure. Now, I want to find the ...
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Evaluating Model Accuracy on a testing data set for a DecisionTreeReegressor Model

I am trying an exercise where I have been asked to "Evaluate each model accuracy on testing data set for a max_depth parameter value changing from 2 to 5". The model here is DecisionTreeRegressor. I ...
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how does xgboost handle inf or -inf values?

all, i am using xgboost for binary classfication. I have infs and -infs in my data due to the fact i am calcaulting ratios from one col and and another e.g. df[col1]/df[col2] , since i have zeros ...
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Decision tree on big categorical dataset

I would like to use algorithm ID3 in order to find a decision tree of my dataset. I would like to see which of the attributes and values lead to the different value of rating (1<= x <= 5). Do ...
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Decision Tree Induction using Information Gain and Entropy

I’m trying to build a decision tree algorithm, but I think I misinterpreted how information gain works. Let’s say we have a balanced classification problem. So, the initial entropy should equal 1. ...
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Problems with decision tree labeling of nodes

Decision trees as we know assigns label to the node based on majority class voting. I am curious to find that what could be the problems with such labeling schemes? Does it lead to overfitting the ...
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Decision Tree gives 100% accuracy - what am I doing wrong?

My assumption is that my training set includes the test set, but I don't know how to change this. ...
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Aggregate SHAP importances from different models (algorithms)

A couple of questions on the SHAP approach to the estimation of feature importance. I would like to use the random forest, logistic regression, SVM, and kNN to train four classification models on a ...
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Visualisation of Random Forest

I am tring to see some part of the random forest. The .dot file is created and converted into png but the png file is too small and the graphs can not be seen clearly. What can I do to see the graphs ...
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Which ML approach to choose for the game AI when rewards are delayed?

Question: Which Machine Learning approach should I choose for the AI of my computer game, where the actions of the AI do not lead to immediate rewards, but delayed rewards instead? About me: I am a ...
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value error of incompatible wrong dimension during training a model

i was implementing a decision tree on a dataset. Before that i wanted to transform a particular column with CountVectorizer. For this, i am using pipeline to make it simpler. But there is an error ...
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Syntax error but nothing appears to be wrong?

I'm trying to create a decision tree, and everything appeared to be going smoothly until I encountered this syntax error. I don't see what the issue is? ...
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How do I find entropy of features having numerical data? [duplicate]

I'm a newbie and I'm writing a decision tree from scratch using entropy and information gain. I understand that entropy is the measure of impurity of a data set and also calculated entropy for ...
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Value on Decision Tree plot

After plotting a sklearn decision tree I check what it says in each box and there is one feature "value" that I am not sure what it refers. The first line will be the column and the value where it ...
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Decision tree with multiple outputs

I have a sample with 10 independent variables (X1, X2, X3 ....), and multiple output labels (y1, y2, y3). Here y1 will depend on X1, X2 y2 will depend on X3, X4 and so on. y1, y2, y3 might or might ...
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How do decision trees decide how to split? [duplicate]

I'm studying decision trees. So far i've understood their properties, how they work and how they select the split to perform (Entropy, Gini index, Information gain ecc...). I didn't understand how ...
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What is the hypothesis space of decision tree learning?

Could you please explain what the hypothesis space for decision tree learning look like? And what is the cardinality of this space?
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Upper bound on size of sample set for decision trees

Say I have an instance space with 4 features and I know that a decision tree with 8 nodes can represent the target function I want to learn. I want to give an upper bound on the size of the sample set ...
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Analysis of Alternating Decision Tree on Weka

I am applying the AD Tree algorithm & this is the tree visualization of the output: I can't understand the values in the decision nodes (-0.4,0.541,-0.882...), How are these calculated? & how ...
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Is it possible for decision trees to consider less features than in my training set? [duplicate]

I was looking at the decision tree algorithm and I wondered that for example if the training set has 20 features but only 5 features are important and classification can be done by using only them ...
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Interpret ability of Random Forest Decisions

Decision trees as we know can be easily converted into rules which increase human interpretability of the results and explain why a decision was made. But in case of the random forest when we have ...
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Dealing with categorical variables in Isolation Forest

Isolation Forest is widely used when dealing with outlier/anomaly detection when we have no labels. The theory behind is that making random split at random points and counting how many splits you do ...
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plotting a decision tree based on gridsearchcv

i was trying to plot the decision tree which is formed with GridSearchCV, but its giving me an Attribute error. ...
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Negative value in information gain calculation through gini index

I am trying to determine the root node for the decision tree on given data annual income target variable has been renamed as ...
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Discretisation Using Decision Trees

I'm new to the machine learning and working on a supervised classification problem. I used discretization process to transform continuous variables into discrete variables. So I followed this ...
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How to Validate Decision Tree model by using *statistical tests*?

I'm reading sklearn Decision Trees reference page. In the advantages section, it is mentioned that 'Possible to validate a model using statistical tests. That makes it possible to account for the ...
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What is the intuitive meaning of “leaf weight” in xgboost

I looked through Tianqi Chen's presentation, but I'm struggling to understand the details of what the leaf weights are, and I would appreciate if someone could help clarify my understanding. To put ...
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Advantages and disadvantages of using classification tree

I was working on a project and was trying to validate my decisions. I wondered why would I want to use a decision tree over more powerful algorithms like random forest or Gradient boosting machine ...
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sklearn decision trees categorical data error [duplicate]

Decision trees should be able to separate a finite number of categorical variables (such as three cuisines, languages, etc.). Is it necessary to OneHotEncode it for ...
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Pruning in Decision trees

Following is what I learned about the process followed during building and pruning a decision tree, mathematically (from Introduction to Machine Learning by Gareth James et al.): Use recursive binary ...
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I want to replace XGBRegressor with a simple model to make feature selection

I will make some for loop on to select the best features by my Data frame is big 10M row and about 50 columns so if i replaced xgb with a single Decision tree would it be the same results for the best ...
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How to Split Continuous Labelled Data?

I've started studying decision trees, and I noticed that the examples online used categorical features to split the data at each node. I'm working on data sets with a binary classification output and ...
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Do I need to transform time with sin/cos if I'm using decision tree algorithms?

According to this post, the time on a 24-hour clock should be decomposed into separate periodic components: https://ianlondon.github.io/blog/encoding-cyclical-features-24hour-time/ before feeding it ...
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Root in Decision tree

In graph theory, each vertex of a tree can be considered as a root. Is there any criteria to choose which feature as the root in order to find a faster result?
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ExtraTreeClassifier does not handle missing values

I am using sklearn.tree.ExtraTreeClassifier. It does not handle missing value in training data. All tree-based algorithms handle missing value internally. So, is ...
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1answer
30 views

Gini index as a labeling strategy for leaf nodes

Can we use the gini index to assign a class to a leaf node? If yes how? As far as I understand the gini index can only be used as a splitting metric.
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Impurity measures in decision trees

I have recently stepped into impurity based criteria for decision trees and I was just wondering why do we really need an impurity based criteria model such as the Gini index? What if we could simply ...
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XGBoost and Random Forest: ntrees vs. number of boosting rounds vs. n_estimators

So I understand the main difference between Random Forests and GB Methods. Random Forests grow parallel trees and GB Methods grow one tree for each iteration. However, I am confused on the vocab used ...
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How to decide who to market? Clustering or Decision Tree?

I am working with a dataset that has enough observations and ~ 10 variables, half of the variables are numeric another half of the variables are categorical with 2-3 levels (demographics) one ID ...
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2answers
55 views

Decision Trees and Feature Selection

I'm trying to experiment with the performance of different machine learning algorithms before and after applying feature selection. I tested SVM, Random Forest, KNN, Linear Regression, and, Decision ...
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Different Decision Tree pruning method

I am trying to learn different pruning methods for decision trees. I have put together a list of methods below. Reduced Error Pruning Cost Complexity pruning Minimum error pruning Pessimistic Error ...
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Decision tree too small

I have a data set of 2300 entries, with 5 variables one of them the dependent variable which is binary. I fitted a decision try using the rpart function in R over ...
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1answer
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What is the scalability of linear regression and decision trees?

Recently I'm studying machine learning algorithms among them linear regression and decision tree so I have a question regarding the scalability of both algorithms. Can anyone provide what is the ...
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How could a neural network classifer for multilclass problem classify only in one class when a decision tree is more balanced and accurate?

I want to create a classifier for a data frame that has four classes. Each line can only have one class. I have two predictive models: a neural network and a tree classifier. But they put everyone in ...
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How to find the dependent variables from a dataset?

I am stuck at where how can I get the most dependent variables based on the mean I have this dataset and when I try to: ...
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upsampling imbalanced dataset in decision tree

I have a imbalanced dataset with 3 output labels with one class with 98 percent and other two classes with 1 percent each. I need to run decision tree on this dataset. Should i be upsampling this ...

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