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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 ...
h_ihkam's user avatar
  • 33
1 vote
1 answer
159 views

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" ...
M. Samir's user avatar
0 votes
0 answers
32 views

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 ...
Madhes Monnish's user avatar
3 votes
1 answer
188 views

How do I interpret probability results in conjunction with my known precision/accuracy/recall scores?

I have a Random Forest Classifier (trained with sklearn) modeling a binary data set. Here's what the configuration looks like (I've tuned it for precision intentionally): ...
Colin's user avatar
  • 133
1 vote
1 answer
31 views

Neural Network Weights - How do they know their position?

I am a copyright scholar so please forgive my ignorance. When weights are stored external to a model what is the mechanism by which the weight knows which neuron or node in a decision tree it is ...
Benjamin White's user avatar
0 votes
0 answers
61 views

How to determine the optimal number of trees in Random Forest?

Here I list possible answers for mine: Do you use the graph for OOB? Do you use any other kind of graph? Do you take a fixed number in default? Do you take in consideration any research paper ...
Anisa B.'s user avatar
0 votes
1 answer
173 views

Optimizing decision tree

I have a question regarding the technique/technology which could be applied for the issue: Suppose I have a rule-based tree or decision tree which predicts a variable Y based on variables A,B,C. This ...
DannyV's user avatar
  • 1
1 vote
0 answers
38 views

How to know which rules were applied to predict one sample in trained decision tree model?

I have trained Random Forest Regressor from sklearn. I am able to return text representation from each Decision Tree rule using tree.export_text (sklearn documentation here). But it shows rules for ...
Paulina's user avatar
  • 15
-1 votes
2 answers
260 views

Recall and Precision ML models

I use decision trees for a binary classification. To evaluate the model, I use K-fold cross-validation, where k = 10. When I run the model n times, I get a relatively constant accuracy across all ...
Jan Jansen's user avatar
0 votes
1 answer
210 views

Does Random Forest Regressor use subset of trees to predict value from given data sample?

I will try to draw a little context to my question from title. I build a Random Forest Regressor from 1000 trees using sklearn. Then I exported all the decision paths along with predicted values for ...
Paulina's user avatar
  • 15
2 votes
1 answer
472 views

Difference among ID3, C4.5, C5.0

The C4.5 algorithm uses information gain ratio instead of information gain like ID3, and it also adds pruning. What does C5.0 add more? Is there any example of code? I looked on the web but there is ...
Iya Lee's user avatar
  • 152
0 votes
1 answer
26 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: ...
Muhammad Usman's user avatar
1 vote
1 answer
146 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....
omike's user avatar
  • 11
0 votes
1 answer
3k 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 ...
Mario's user avatar
  • 527
5 votes
1 answer
276 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 ...
RyRy the Fly Guy's user avatar

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