Search Results
Search type | Search syntax |
---|---|
Tags | [tag] |
Exact | "words here" |
Author |
user:1234 user:me (yours) |
Score |
score:3 (3+) score:0 (none) |
Answers |
answers:3 (3+) answers:0 (none) isaccepted:yes hasaccepted:no inquestion:1234 |
Views | views:250 |
Code | code:"if (foo != bar)" |
Sections |
title:apples body:"apples oranges" |
URL | url:"*.example.com" |
Saves | in:saves |
Status |
closed:yes duplicate:no migrated:no wiki:no |
Types |
is:question is:answer |
Exclude |
-[tag] -apples |
For more details on advanced search visit our help page |
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.
4
votes
Accepted
How can I train a decision tree constrained to have number of decision nodes = tree depth?
The structure you want seems to be expressable with an ordered series of if, else if, ... statements. This is a common structure for interpretable models, often called a Rule List, or Decision List. I …
2
votes
Accepted
What happens to a machine learning technique (specifically Decision Tress and Logistic Regre...
There are no categorical value support in the decision trees used in scikit-learn. Either the values are just numbers, or they have been one-hot-encoded.
One-hot encoding with an unseen input value w …
1
vote
Classify sensor data (multivariate time series) with Python's scikit-learn decision tree
For usage you need to flatten the 2D raw sensor data into 1D features. Below code demonstrates the basics.
What kind of feature engineering to apply for best predictive effect depends entirely on the …
1
vote
Details on soft decision trees
This is likely the Exponential Moving Average function.
In simplest form it is
ma_new = alpha * new_sample + (1-alpha) * ma_old
Where alpha is the parameter that controls rate of decay, between 0.0-1 …
0
votes
a simple way to test wether a tree-based classifier would transfer well to a target population?
I would train new models and use that to partition the samples, then do a exploratory data analysis on these sets of samples.
For instance train model B-only on population B and look at samples inter …
0
votes
Is it feasible to use decision tree algorithms for sensor fault detection?
You need to determine how to formulate your problem. I see it as having two aspects:
1. Detect an abnormal value (in temperature)
2. Determine whether abnormal value is due to sensor or system problem …
1
vote
Accepted
Scikit-learn decision tree in production
Persisting the model parameters is the only out of the box solution in sklearn. You should ensure that:
Your model is reproducible from data
The versions of dependencies used (incl sklearn) is locke …
16
votes
Accepted
Multicollinearity in Decision Tree
Desicion trees make no assumptions on relationships between features. It just constructs splits on single features that improves classification, based on an impurity measure like Gini or entropy. If f …
1
vote
Isolation Forest Prediction Mechanics: Does it compare value with every tree (and the origin...
You can find the sklearn implementation of Isolation Forest in Python at https://github.com/scikit-learn/scikit-learn/blob/a24c8b46/sklearn/ensemble/iforest.py#L229
It calculates the mean path depth …