Questions tagged [decision]
The decision tag has no usage guidance.
31
questions
1
vote
1answer
30 views
Convert a categorical variable to a numerical variable for decision tree [closed]
I have a set of medical records, one of the columns has the name of 2000 different doctors. What do I need to do in order to convert these strings to numbers? I want to use a decision tree.
7
votes
1answer
138 views
Decision rule as a hyper-parameter in LASSO
I have a question that is related to the following: Is decision threshold a hyperparameter in logistic regression?
but would like some clarification.
The general consensus is that the decision rule ...
0
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0answers
17 views
0
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0answers
6 views
repeatedly select the character in the regression decision tree using least squares method
We know that for the given training data
$$D = \{(x_1, y_1),\cdots,(x_n, y_n)\}$$
here $x_i = (x_i^1,x_i^2,\cdots,x_i^m),$
to build a regression decision tree,
...
0
votes
0answers
33 views
Decision tree - least square empirical improvement
I'm looking for an explanation of formula (35) of the Gradient Boosting paper of Friedman [Friedman 2001, Greedy function approximation: a gradient boosting machine].
Here the least-squares ...
0
votes
0answers
41 views
Statistical model suggestion for binary decision problem
I am looking for a statistical/machine learning model, which can describe and predict a (forced) binary decision between say A and B at any moment in time.
I have input data from time series of say 3 ...
3
votes
1answer
37 views
can we use any learners in gradient boosting instead of trees?
As we are simply trying to predict residuals from weak learners and aggregating them, can we use any weak learners in gradient boosting machines instead of trees ?
If so, why are the all the gbm ...
3
votes
1answer
48 views
Scikit's permuted features in decision tree implementation
In the Scikit's docummentation of decision trees I found a note:
"The features are always randomly permuted at each split. Therefore, the best found split may vary, even with the same training data ...
0
votes
0answers
12 views
Hierarchical simulation of patient waiting times in order to keep annual average below some threshold
I have a dataset about patient waiting times in a healthcare district. These data have 3 categorical variables:
- healthcare provider;
- healthcare service (eg. cardiology visit, electrocardiogram, ...
0
votes
1answer
44 views
What does this ROC value mean?
What does this roc value mean? How do I interpret it? Are there values which help in inferring it like in case of kappa?
1
vote
1answer
179 views
Sucessive stimuli effect on auditive perception
My experiment is about auditive perception, and consists of playing sound at A Random locations (1-6), B Different frequencies (1-6), C is a Choice location (1-6 proposed choices).
Participants were ...
1
vote
1answer
309 views
What to do if a specific parametric survival model does not converge but most other model do (implications for decision analysis)?
Please consider the following:
In (health) decision modelling, an often-used approach is to extrapolate observed survival data with parametric functions.
The NICE Technical Support Unit summarised ...
-1
votes
1answer
135 views
Interpretation of regression tree with Poisson data
Above is a decision tree made by following code
...
2
votes
0answers
31 views
Determining the decision boundary for Naive Bayes
I'd like to know if this is a sensible idea and if there exist any already formed methods to do this (I'm new to the data science area).
Essentially, I have used Naive Bayes to accurately classify ...
0
votes
1answer
118 views
Create a Complex Decision making AI (NEAT) from Scratch
I'm pretty new to the world of neural networks, so I'm asking this question, I'll explain in the process all the words used in the title so, if there's something unclear or wrong, I'll edit the ...
0
votes
1answer
15 views
Decision Tree on a set with reliabilty information
I've got an introductory AI course in my university, and I was taught about decision trees. I'm now facing a classification problem that seems solvable with a DT, but I'm stuck with an unseen ...
1
vote
0answers
126 views
Rpart and subsets
I am getting unexpected results from an rpart model, where the model selects two variables, one of which is a subset of the other. This in itself is not unexpected, but the seemingly odd thing is that ...
0
votes
1answer
50 views
Classification Tree or Regression Tree?
I have time series data: students that learned in groups for minimum 3 times and maximum 10 times and for each learning group session had to state if they faced a motivational OR a cognitive problem, ...
3
votes
1answer
69 views
Advertisment decision making based on customer past behaviour
Problem description: Every 3 weeks a fashion company sends out an expensive booklet with descriptions of clothes to each customer on their electronic records. There exists a purchase history what each ...
2
votes
0answers
136 views
Calculating Probability for Decision Tree Model
I came across calculation of probability for a decision tree model - which I do not understand.
As I plan to do CEA of some health interventions I would not like to mess it up.
The used method (...
0
votes
1answer
32 views
What is the optimal strategy to invest a limited resource in N uncertain options?
Suppose I have a £100 to invest in 2 options. Each option has a expected value, but the value is unknown.
Option A has the highest average expected value, but a bigger uncertainty range.
If we just ...
2
votes
2answers
155 views
Contributing predictors to a response variable
I have a dataset which has the following two tables which look like the following:
...
0
votes
1answer
42 views
SVM decision non linear
As I understand, to perform a decision in a non linear case (using a kernel) I use the following:
$f(x) = sgn(\sum_{i=1}^{n} y_{i} \alpha_{i} \boldsymbol{k}(x,x_{i})+b)$
Where i=1,..n are the ...
0
votes
1answer
725 views
1
vote
0answers
24 views
Scaling weights in multi attribute decisions
I have a set of attributes that make up a feature (e.g 'Accommodation' in the sample below), each attribute has a weight range specific to the feature as per the picture.
The intention is to have ...
10
votes
1answer
15k views
What is the difference between decision_function, predict_proba, and predict function for logistic regression problem?
I have been going through the sklearn documentation but I am not able to understand the purpose of these functions in the context of logistic regression.
For ...
1
vote
1answer
67 views
Why adding new variables decreased the decision tree model's accuracy?
I am data modelling analyst in telecom company and now work on churn prediction model. I use decision tree algorithm with cross validation in SAS Enterprise Miner. The results are satisfactory as I ...
0
votes
1answer
85 views
Decision making under uncertaintly
In decision making under uncertainly we have these criterion
1- maximin criterion
2- minimax criterion
3- maximax criterion
Now I want real life example to illustrate all of these criterion (I ...
0
votes
1answer
89 views
step by step explanation of answer for discriminant function [duplicate]
This is a question/answer from my first assignment (intro class on pattern recognition)
I don't understand how they used the p(x given w1) and p(x given w2) in the discriminant function. For example, ...
-1
votes
2answers
383 views
how do you combine the weak models in gradient boosted tree?
In this article
https://www.analyticsvidhya.com/blog/2015/11/quick-introduction-boosting-algorithms-machine-learning/
The author indicates you combine 3 weak models into a final one using gradient ...
0
votes
1answer
60 views
What does a decision tree with both GOOD outcome means?
I have a decision tree built in R using rpart() from rpart package.
However, when following the nodes, we have one condition leading to both outcomes as GOOD. This is weird for me.
What does that ...
