Questions tagged [decision]

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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.
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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 ...
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0answers
17 views

Decision boundary of bayes classifier

How do you solve the folllowing?
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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, ...
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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 ...
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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
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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
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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 ...
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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, ...
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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?
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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 ...
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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 ...
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1answer
135 views

Interpretation of regression tree with Poisson data

Above is a decision tree made by following code ...
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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 ...
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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 ...
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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 ...
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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 ...
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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
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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 ...
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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 (...
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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
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2answers
155 views

Contributing predictors to a response variable

I have a dataset which has the following two tables which look like the following: ...
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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 ...
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1answer
725 views
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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 ...
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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 ...
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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 ...
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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 ...
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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, ...
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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 ...
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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 ...