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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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UnderBagging Testing in Matlab

I used UnderBagging for an imbalanced dataset with 45700 observation with 20 fetures. 45000 observations are 1 and 700 are 0. I used UnderBagging for classifier C ( for example for Decision Tree). I ...
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Is there any source for Decision Tree Algorithm which is based on loop and not recursion? [on hold]

I am working on a variation of Decision tree and need to implement the tree part. I have implemented it using recursion but sometimes it's failing. My question is there any implementation of decision ...
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Will the combination of Chaid/CART tree modelling improve the accuracy of the Decision Tree Regression Model?

Will the performance of the decision tree regression model significanlty improve if we consider CHAID modelling first by identifying the key continous/categorical dependent variables and then builidng ...
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Isolation Forest Score Function Theory

I am currently reading this paper on isolation forests. In the section about the score function, they mention the following. For context, $h(x)$ is definded as the path length of a data point ...
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Confidence vs. Count in association rule mining: which one is better?

I am writing a program that mines association rules from a large data set. I have an array of association rules, and I have to decide which ones are more representative of the patterns I am studying. ...
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Training xgboost model with more data having different characteristic

I have trained my model for ECG data which has 8528 ECG files having length 30s and sample rate 300 so total file length in csv ...
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Decision trees With branching Factor?

How to construct a Binary Tree from a decision tree with branching factor greater than 2 (b>2)?
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Categorical Variables in Decision Tree

I was going through the Andrew Ng's notes for Decision Trees. It has one section explaining the usage of categorical variables using Decision Trees in which I am not able to understand this part " A ...
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What happens to a machine learning technique (specifically Decision Tress and Logistic Regression) if the validation dataset has a new category?

Let's suppose I have a dataset which has a categorical variable and the problem I am solving is a classification one. This categorical variable var has ['A','B','C'] as the possible set of data. ...
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RandomForestRegressor intermittantly returning a single prediction

BACKGROUND I have a RandomForestRegressor from scikit-learn which, for each example row, takes in four float features and ...
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DecisionTreeClassifier for multi label classification giving outputs as single classes

I have built a DecisionTreeClassifier for multi-label classification with details given on https://scikit-learn.org/stable/modules/multiclass.html with input data in shape ...
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How to build a symptom checker and medical diagnose chat bot

I want to build a chatbot which can diagnose an illness depending upon the symptoms which are given to it. I want to show you an example scenario of how it should work; Application - A , User - U ...
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Finding optimal region of a continuous search space

I'm working on a problem in which I've got large table of data with two features and three target columns and I need to find the optimal filter parameters which theoretically would give me: 1) most ...
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Number of iterations for minimal cost complexity prunning?

I've been fiddling with weka's J48 decision tree implementation (C4.5). My goal is to implement cost complexity prunning using weakest link cut method. Basically my algorithm iteratively prunes the ...
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Wrong calculation of feature importance of decision tree in R

I trained decision tree both in python and R, but I think the way feature importance is calculated in R may be wrong. Following is the sample code which you can use to reproduce the problem. Let's say ...
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What is the differences in the Gini Index, Chi-Square, and Information Gain splitting methods?

I am looking through decision trees, and I do not understand what makes each of these methods different. Could someone explain clearly what the difference between these is? Thank you.
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Classify sensor data (multivariate time series) with Python's scikit-learn decision tree

i'm trying to apply scikit learns decision tree on the following dataset with the goal of classifying the data: sensordata: multiple .csv files every .csv file has multiple sensors (see here) each ....
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What does it mean to take the “average” of two decision trees by 'voting'

I have heard, in relation to random forest algorithm, that the algorithm will fit many decision trees and take the average of them by votes. (This is related to bagging as well) I understand what ...
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Choosing the first node in a decision tree, basic example

I'm wondering whether I'm understanding the process of choosing a node correctly and would like to see if this example makes sense. using the following data : ...
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Why can decision trees have a high amount of variance

I've heard that decision trees can have a high amount of variance, and that for a data set $D$ split into test/train the decision tree could be quite different depending on how the data was split. ...
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why do we need row sampling in random forests?

In random forests, where our estimators are decision trees, we do column (feature) sampling without replacement within an estimator, and with replacement in between estimators. This is perfectly fine ...
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How to determine number of leaves in decision tree analysis?

Would be grateful if some expert on the forum can help me understand how to decide optimum number of leaves in a decision tree analysis. I am using SAS and if I supply leaves=6 in my model then miss-...
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Not able to interpret decision tree when using class_weights

I'm working with an imbalanced dataset. I'm using a decision tree (scikit-learn) to build a model. For explaining my problem I've taken iris dataset. When I'm setting class_weight=None, I understood ...
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Compile See5 / C50 GPL Edition

See5 / C5.0 is Data Mining Tools available from rulequest I want to compile C50 for Linux, preferably for CentOS 6.x, but I am unable to compile. I have also tried on Ubuntu, but not success there as ...
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Cross Entropy vs Entropy (Decision Tree)

Several papers/books I have read say that cross-entropy is used when looking for the best split in a classification tree, e.g. The Elements of Statistical Learning (Hastie, Tibshirani, Friedman) ...
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Grid search model isn't recognized as fitted for Graphviz

I find this really weird, and the code is really straight forward. What am I doing wrong ? ...
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How does L1 Regularization work in lightGBM

From the paper, lightGBM does a subsampling according to sorted $|g_i|$, where $g_i$ is the gradient (for the loss function) at a data instance. My question is that, when the objective is L1 loss/...
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Decision Tree - Preprocessing for very sparse features

How do we pre-process data for very sparse features for a decision tree? From this Turi documentation for decision trees It mentioned this: Why chose decision trees? Different kinds of models ...
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how does splitting occur at a node 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 way of creating ...
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model.score and r2_score giving different values for a regression model

I am build a linear regression model and a decision tree model using sklearn. I want to compare the performance of these two models, I have calculated the r2_score for both the models. I have ...
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What is disadvantage of using CART in regression problems?

Why is CART hardly used for regression? Is there any significant reason for its unpopularity in regression techniques?
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Using Decision Trees to interpret good factor values

Decision trees are often used in machine learning as classifiers/regression models (CART), or in ensemble methods (Random Forest) etc, where predictive accuracy, minimizing bias and variance are the ...
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Get Decision Tree Prediction With Random Forest

If I give random forest parameters as RandomForestClassifier(n_estimators=10,bootstrap=False,max_features=None,random_state=2019) Should it be creating 10 same ...
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Building a minimal encoding of nominal labels from numerical features

I have a set of ~500 objects, only a single instance of each kind, and each has ~500 numerical features ... I need to find the minimal encoding which allows to identify objects. I am interested in ...
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Is it possible to have a better performance with C4.5 than Bagged tree?

I am not sure but I have read that bagged trees are used to improve the accuracy of a single tree methods such as C4.5 but applying both of them over the same dataset I got better accuracy with C4.5, ...
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How to extract the sample split (values) of decision tree leaves ( terminal nodes) applying h2o library

Sorry for a long story, but it is a long story. :) I am using h2o library for python to build a decision tree and to extract a decision rules out of it. I use some data for training where labels get ...
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Use prediction as feature for a decision tree

I'm working at classifying documents according to their content. First I built a decision tree model that gives 90% of goods results. Then I tried a TFIDF/SVC approach which also gives 90% of good ...
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receive value error decision tree classifier after one-hot encoding

I am trying to build a decision tree model. After one-hot encoding, it seems somehow the data still has a problem. When I run the following code, I receive this error: ...
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SciKit-Learn Decision Tree Overfitting

I'm pursuing a computer science minor at my university, and one class I'm in is Machine Learning. We have a project to utilize a few algorithms we have learned so far. I've been using SciKit-Learn to ...
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Assessing performance of an agent based on commission rate, market share and revenue

I have a set of data for agents selling properties (apartments) for a company in different states. The company would like to assess the performance of the different agents given the following: Number ...
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Will unnecessary features harm the tree based model?

Is it necessary to drop noisy features (eg column of random numbers) from tree features? I think it's not. sometimes it may benefit but will never cause any harm to the model. Because at each split ...
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Insights from RandomForestRegressor (or any RF output)

Beyond the feature importance calculations from a RandomForestRegressor in sklearn, what are some good strategies to draw ...
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Decision Tree Classifier For Minimizing Arbitrary Cost Function

Assume the input $X$ of $n$ data points, and $m$ features. Also, assume I have four different Heuristic algorithms ($h_1$, $h_2$, $h_3$, $h_4$) that are obtained independently of $X$. Problem: ...
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Desicision tree classification with a “false” attribute

This is a pretty specific problem but I think it can help me understand better the whole concept of the subject. A doctor in the hospital is in charge of 20 medical students. For every patient, the ...
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2answers
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Accuracy differs between MATLAB and scikit-learn for a decision tree

Is there any possibility to vary the accuracy of same data set in matlab and jupyter notebook by using python code ? For same data set, at first I applied it in matlab and get 96% accuracy for ...
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Should we invest more in exploration (as opposed to exploitation) when the distribution is fat-tailed in contrast to a bell-curve?

Not sure if this falls under data science but here it goes: In the exploration/exploitation trade-off (deliberation vs commitment to one choice), should we invest more in exploration when the ...
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39 views

WEKA Random Forest J48 Attribute Importance

I have been using WEKA to classify very long duration audio recordings. The best performing classifiers have been Random Forest and J48. The attributes used to classify the audio are acoustic indices. ...
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Why does Feature Importance change with each iteration of a Decision Tree Classifier?

After applying PCA to reduce the number of features, I am using a DecisionTreeClassifier for a ML problem Additionally I want to compute the feature_importances_. However, with each iteration of the ...
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How to represent linear regression in a decision tree form

I have read that decision trees can represent any hypothesis and are thus completely expressive. So how do we represent the hypothesis of linear regression in the form of a decision tree ? I am ...