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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Summarize KPI Results with NLP
I am working to create a model that can derive the drivers of the current month numbers based off of historical trends (same month prior year, 3-month run rate, prior month, etc.) to baseline against. ...
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My tree based models keep overfitting
This is a project of multi classification. Each model severely overfits. Decision Tree, Random Forrest and especially XGBoost. And the classification report reflects that. where the csv is https://...
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LightGBM One Full Tree
For a writing a test case I trying to fully understand LightGBM. I would have thought that the following example would build a tree that is capable of completely overfitting the data. What parameters ...
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If min_sample_leaf is greater than min_sample_split in decsion tree will it be a problem?
I am tuning the hyperparameter of the decision tree for a data set of 550 samples. As I am comparatively new in hyperparameter tuning(I am learning and implementing), I am confused about what values ...
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Why might transforming my features improve the performance on a simple decision tree?
The features & target in my dataset are very skewed. Could anyone explain why transforming the features & target (I'm using a Yeo-Johnson transformation) is significantly improving the ...
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Decision Tree from Expert Experience
I am looking for decision trees which are hand-crafted by domain experts from their experience. As far as I know, medical practitioners have their empirical decision tree for diagnosis, I want to know ...
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Best methods to stratify data into 4 groups (unsupervised manner) using a set/combination of variables
I'm trying to stratify a set of patients according to possible molecular subtypes of cancer. Now, I know all these patients have a type of cancer, but the goal is to (in a unsupervised manner) cluster ...
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Very low AUC in the test data while having adequate accuracy on the test data in Random forest model
I am encountering a strange issue where I obtained low AUC = 0.18 on a test data whereas the model I built gave an accuracy of 80.3 on this test data, and sensitivity of 55.5 and specificity of 90.7 I ...
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How do sklearn's trees evaluate NaNs on inference?
Imagine we have fitted a sklearn.tree.DecisionTreeClassifier object like this one: If we wanted to predict the class of this observation: ...
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LightGBM binary classifier: how can I know which values of categorical feature offer the most discrimination?
I am familiar with using methods such as lgb.plot_split_value_histogram to investigate the most used splitting points along numerical features. Is there an ...
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Only one node generated after using decision tree model on training data set
I am trying to build a decision tree model predicting an outcome variable (named : Results) based on predictor variable. Indeed, I have applied one-hot encoding on some of the ">2 level" ...
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In natural language processing, what is the name for the technique in which a sentence is modeled as a tree in order to generate simpler sentences?
In natural language processing, there are times when we model a complex and/or compound sentence as a tree (or hierarchy) of simpler sentences. The tree-model (hierarchical model) can help us ...
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Latest Tree-based models
What are the latest Tree-based models that are used in machine learning? Tell the new models except the old ones such as the Decision tree, Random Forest, Gradient Boosting, LightGBM, XGBoost, and ...
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Average training instances sampled with bagging
The book Hands-On Machine Learning has a section on Out-of-Bag Evaluation related to Decision Trees, where it's stated that, By default a BaggingClassifier samples m training instances with ...
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I want to create a system for classifying bone fractures What pre-processing steps can I use to process images?
I want to know where I should put the image preprocessing code in the decision tree code How to extract features from images and classify them
