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.
734
questions
0
votes
1
answer
10
views
Calculating feature importance with Scikit-Learn's Decision Tree Classifier
I am attempting to determine the most useful bands of a multiband image classification (i.e. Red, Green, Blue, Near Infrared, etc. used for classifying pixels) and wrote the following function to ...
0
votes
1
answer
15
views
How to represent varying reliability of ratios calculations in a dataset?
I want to predict whether the client will renew his/her subscription based on groceries consumption patterns. Suppose an order contain only one type of grocery.
I have a DataFrame containing ratios of ...
1
vote
1
answer
26
views
Distilling a Random Forest to a single DecisionTree, does it make sense?
I stumbled into this blog which shows how a decision tree trained to overfit the predictions of a properly trained random forest model is able to generalize in pretty much the same way as the original ...
- 35
0
votes
1
answer
14
views
How do I use a column with data of different layers for AI?
I am working with real estate data for an ML/DL project. In the csv file there is a column in which each cell contains data like the examples below:
...
0
votes
0
answers
9
views
Feature scales and feature importance
Tree-based algorithms do not require feature scaling before fitting, and
I am working on gradient boosted tree models (and random forest) without scaling features.
I'm curious if feature scaling ...
- 101
0
votes
0
answers
8
views
Overlapping values of a variable in decision tree
Is it okay to have a variable, this variable has values which are subsets of others in the same variable when building a decision tree?
To be specific, I am working with a dataset that have a variable ...
0
votes
0
answers
5
views
Value[] attribute in my decision tree is not consistent with number of samples
I read that value[] attribute in a decision tree shows the distribution of the samples across class 1 and class 2.
However, my value[] is not adding up. In the root node for example, there are 14 ...
- 1
1
vote
2
answers
108
views
Can decision trees handle Nominal Categorical variables?
I have read that decision trees can handle categorical columns without encoding them.
However, as decision trees make splits on the data, how does it handle Nominal Categorical variables?
Surely a ...
- 557
0
votes
1
answer
23
views
DART algorithm implementation. Converting mathematical notation to pseudocode
I am learning how DART algorithm (https://arxiv.org/abs/1505.01866) works and I want to implement it in C#
I have the algorithm's description in mathematical notation and I don't understand most of it....
0
votes
1
answer
19
views
0
votes
1
answer
22
views
Noisy Data Robustness - NN vs Decision Tree
We are working on a physiological marker predictor using hospital patient data. We use a boosted decision tree-type algorithm, which seems to be very sensitive to the noise in the training data. Would ...
0
votes
0
answers
15
views
Is it possible to capture time-specific outliers using a Decision Tree?
Having this data set
...
0
votes
1
answer
50
views
how do I test if overfitting exists when I use cross_val_score method?
I got the following code form a book on xgboost.
I wonder whether this is a correct way of analyzing cross validation score for overfitting purposes.
mean accuracy is 81 which can be okay. but what if ...
- 21
0
votes
1
answer
143
views
How do the splits points in a decision tree within Random Forest are taken/selected? (Base on which criteria?)
I checked many posts to figure out how random forest (RF) learning algorithm (an ensemble of many decision trees (DT) constructed by Rain forest algorithm) within bagging select split points at each ...
- 335
2
votes
1
answer
52
views
Surrogate splits in Python
I want to use RandomForestClassifier from Sklearn to predict categorical variable (credit risk). But one of the predictors seems to have missing values:
...
- 61
2
votes
1
answer
64
views
help interpreting training/validation curves for classification tree
I'm developing a binary classification tree and having some touble interpreting my training/validation curves. I used the CART algorithm with information gain as my splitting criterion. The training ...
- 121
0
votes
0
answers
13
views
Why are decision trees driven by the Gini impurity as opposed to the accuracy? [duplicate]
It seems that most implementations of decision trees use the Gini impurity as their partitioning criterion. Why isn't accuracy used instead, since it's a more widespread metric across different ...
- 203
0
votes
1
answer
41
views
DecisionTreeClassifier cannot take one-hot encoded classes?
I got ValueError: Found array with dim 3. None expected <= 2. I dont know which array has dim 3?
DecisionTreeClassifier cannot take one-hot encoded classes?
But ...
- 165
0
votes
0
answers
25
views
About feature importance in deep learning
For tree methods, I can plot the feature importance plot from tree.feature_importances_ in sklearn, is this achievable in deep learning (neural networks)?
Is there ...
- 165
1
vote
0
answers
28
views
DecisionTreeRegressor with criterion='poisson' not recognizing perfect separation
I created a minimal example of Poisson decision tree regression as such
...
1
vote
1
answer
27
views
How to use labels to fit several thresholds in a simple decision rule?
I have a binary labelled dataset with numeric features. I want to create a "business rule" of the type y = x1 > t1 and x2 > t2 and x3 > t3. ...
- 1,160
0
votes
0
answers
9
views
How to compensate for different feature sampling in decision trees
I have a dataset, on which I would like to use a decision tree, where some features are sampled much less frequently than others. I am concerned that they could lead to suboptimal feature selection in ...
- 1
0
votes
0
answers
21
views
Can the product of tree regressions be represented by a single tree?
Assume that we have two separate tree regressions. I'm interested in understanding whether the product of tree regressions can be represented by a single tree. Would this be possible?
- 23
0
votes
0
answers
13
views
What is the use of validation dataset when doing regression-based outlier detection?
I have a dataset where data are velocity data splitted as:
60%(train - non-anomalous)
20%(validation - 50% of it anomalous)
20%(test - 50% of it anomalous).
From my understanding, when doing outlier ...
- 121
4
votes
2
answers
242
views
Loss for ordered multi class data in classification
Assume data which is labeled $y_i \in \left\{ 1, 2, 3, \ldots, 9, 10\right\}$.
Assume the labels are ordered, namely, given $y_i = 10$ to estimate $\hat{y}_{i} = 1$ is much worse than $\hat{y}_{i} = ...
- 141
0
votes
0
answers
56
views
Best way to handle missing values with XGBoost?
I know of a number of ways to handle missing feature values, and wanted to get folks' input on what might work best. My end goal is to be able to predict accurate probabilities for a binary ...
- 101
0
votes
0
answers
10
views
Build decision tree with fixed features, but learn optimal value of features?
I'm trying to build a hybrid between an expert system and a decision tree model to serve as a baseline for transparent comparison with more sophisticated models. My problem is as follows:
I have a ...
- 1
0
votes
1
answer
100
views
Decision tree vs logistic regression feature importances
I have trained Logistic regression and decision tree in skearn on the same standardized dataset (binary classification).
Top important coefficients for the decision tree are (sorted by ...
0
votes
0
answers
26
views
Understanding Cost-sensitive Decision Trees Behaviour
I have a binary classification problem with imbalanced data and am attempting to use cost-sensitive learning to handle the imbalance. I have used LogisticRegression, LinearSVC, SVC and DecisionTree ...
- 387
3
votes
1
answer
203
views
Why am I getting the exact same results with both a Logistic Regression and Decision Tree Classifier?
I am working on a binary classification problem and am using sklearn's logistic regression model and decision tree classifier.
Somehow I am getting the exact same results and accuracy score on both.
I ...
- 31
0
votes
0
answers
25
views
My overfitted decision tree regressor gives better result than pre-pruned tree?
I create a decision tree regressor without giving any parameters. The resulting tree has 6255 leaf nodes (out of 6348 entries of train set) and depth of 39. Most probably it has overfitted. But its ...
- 1
1
vote
1
answer
46
views
N-ary decision tree with categorical features
I want to build an n-ary decision tree with categorical features.
I am using ordinary ID3 algorithm to build a tree.
Lets take the next dataset as a training dataset for building a decision tree:
...
- 111
0
votes
1
answer
200
views
How can I train a decision tree constrained to have number of decision nodes = tree depth?
In order to make a classifier dead easy to understand/interpret, I want to classify tabular data (with n columns) according to a set of nested rules, with the ...
- 411
0
votes
1
answer
23
views
How do I use one hot encoding with 240 nominal variables and each with equal occurrence?
The method I saw that's generally used to deal with large # of nominal variables is to keep the most frequent variables and introduce a new "other" category. But that's not possible with my ...
- 1
0
votes
1
answer
27
views
How to properly use regression / tree based models for time-series data
Regression/tree-based models appear to treat each prediction as a memoryless process, namely given a feature vector $\hat{x}_i$, predict $y_{i+1}$, but previous states $\hat{x}_{i-1}$, $\hat{x}_{i-2}, ...
- 11
0
votes
0
answers
30
views
When you plot features(pairplot) and gets many vertical plots, which non-linear regression would be useful to make sense of it?
I have plotted my N features against the output and I have multiple vertical plots and was wondering, if I should go by decision trees.
- 101
0
votes
0
answers
17
views
compare decision tree vs extended gradient boosting mathematically?
If we want to compare decision tree vs extended gradient boosting vs xgboost mathematically, what are their differences?
- 1,754
0
votes
0
answers
36
views
What is the difference between using Minimal Cost-Complexity Pruning and testing all possible tree depths in a decision tree?
I´m studying the sklearn decision tree classifier and I´m having some trouble understanding the concept of pruning. From what I understand it consists in making the tree less deep in order to avoid ...
- 1
0
votes
2
answers
47
views
Can Gradient Descent boosted decision trees miss the forest for the trees?
My understanding of this stuff is pretty basic so my semantics may be off, but bare with me.
XGBoost and other gradient descent packages make the best possible split of the data right off the bat. ...
- 113
0
votes
1
answer
322
views
Splitter in decision trees in sklearn implementation
I am very confused about how decision trees select features and threshold within each feature to do the split. I totally understand the different splitting metrics (Gini index and so on) used and how ...
- 35
0
votes
0
answers
57
views
is time series data normalization for xgboost required?
According to the developer - xgboost does not require feature normalization https://github.com/dmlc/xgboost/issues/357
no you do not have to normalize the ...
- 101
0
votes
1
answer
98
views
how to compute the possible number of splits in decision tree?
In the following dataset, if we want to include just two variables, STORE and PctDiscMM, in a classification tree model, what is the possible number of first splits?
...
- 1,277
1
vote
4
answers
309
views
Draw a decision tree with depth 2 that is consistent with the data
I am trying to come up with a solution to this for an exam preparation but cant come up with anything, dont know how to tackle it... if i use information gain the depth increases beyond 2.
What would ...
2
votes
1
answer
59
views
Model from an aggregate
I’m in a place where we’re unable to train models on data due to GDPR. What I want is to predict people getting a job (y) given (x,x,x,x…) their employment type working full time or part time, work ...
- 123
1
vote
1
answer
321
views
Online Learning/Continual Learning for tree-based Algorithms
Every example I come across any kind of iterative learning on Random Forest/XGBoost/LightGBM, it just continuously grows the number of estimators for new batches of data by ...
- 328
0
votes
1
answer
975
views
1
vote
0
answers
8
views
Precision and AUROC for which class values
I am a newbie in reading research paper and implementing it by myself. I went through the paper Breast Cancer Survival Prediction from Imbalanced Dataset with Machine Learning Algorithms.
Can anyone ...
- 359
1
vote
1
answer
191
views
Decision tree implementation in python that correctly handles categorical variables
Is there a Decision tree implementation in python that correctly handles categorical covariates?
By "correct" I mean that it is able to send any subset of category levels down one daughter ...
- 749
1
vote
0
answers
26
views
ML Model that doesnt average/penalize extreme values
I have a 20k dataset, and a couple hundred of those lines are extreme values and 10 of them or so are even extremer values. But they are correct and have a unique tag, so when that tag comes up I am ...
- 11
0
votes
0
answers
31
views
Is it possible to extract precise decision boundaries from a random forest for a multiclass classification?
I have a random forest (argmax post-processor) with 3 trees and 10 input features. The final outcome of the random forest is either true or false depending on the combination of the feature values. Is ...
