Questions tagged [decision]

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Building a Customer Behavioral decision tree... seeking recommendations

I'm looking for some recommendations on how to model the following problem: I'm in the context of the retail industry and I want to model how the customers based their decisions at the time of picking ...
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How to fix error in Decision tree prediction in R?

I am unable to fix the code for decision tree prediction. So, my dataset includes 20 variables. head(newdf) Then, I partitioned my data and created a decision ...
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At what value of p are you indifferent between action A and action B?

Problem statement: Suppose you are deciding between two actions, A, and B, and are testing between two mutually exclusive hypotheses, H1 and H2. If you choose action A, you receive 1 dollar if H1 is ...
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Find a decision rule using Rao-Blackwell Theorem

Suppose that an observation $x \in (-1,1)$ comes from a sample model with a parameter $\theta$, with density function: $$ f(x\mid\theta) = \begin{cases} \theta\ &\text{if}& -1 < x < 0\\...
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Normalize binary variable in MCDM?

I have some data with a couple of binary but also continous variables. For MCDM, there are different normalization techniques such as Max, Max-Min or Vector Normalization. However, I'm wondering if it ...
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Which R package should be used to perform sensitivity analysis for multi criterion decision making models?

I have 10 criteria for which I want to do sensitivity analysis. I have used the AHP method to calculate the weights for each criterion. I am not sure how to perform sensitivity analysis for multiple ...
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Are binary splits always better than multi-way splits in decision trees?

I'm trying to devise a decision tree for classification with multi-way split at an attribute but even though calculating the entropy for a multi-way split gives better information gain than a binary ...
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1 answer
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Combining DecisionTreeClassifiers

I have an array of sklearn.tree._classes.DecisionTreeClassifier classifiers that are used in a boosting algorithm, so the final classifier is a weighted sum of these individual trees. The problem is ...
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What is the purpose of pruning a decision tree?

What are the precise benefits of pruning a decision tree. What are the alternatives to pruning. When is it advantageous to do the alternative?
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Can you use a decision tree to predict a numeric variable? Is a decision tree that predicts a numerical variable a regression tree?

I am pretty sure that decision trees are only used to classify categorical variables. But I was wondering if there was a way for one to predict a numeric variable? If there is ... what is the method ...
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238 views

What is the relation between Linear Classifier and Linear Decission Boundary (or Non Linear Decision Boundary)?

As we know (Wikipedia Definition): Linear Classifier makes a classification decision based on the linear combination of the feature vectors. Mathematically : $y = f(\sum w_i x_i)$ So , $f$ is our ...
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Confidence Level for mean comparison among control and treatment group

I collected data where two players interacted with two choices each, where a 2x2 table resulted when representing the game in strategic form. The 4 outcomes are (A, A) ; (A, B) ; (B, A) ; (B, B). My ...
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decision tree classifier's square brackets

I am learning the decision tree classifier. The class here is whether or not a person will accept a loan offer (orange= not accept, blue=accept). I don't understand what do values inside the square ...
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170 views

Cost complexity pruning decision trees

I am trying to understand cost complexity pruning in classification trees. I found that DecisionTree in sklearn has a function called cost_complexity_pruning_path, which gives the effective alphas of ...
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1 answer
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Two question about a decision tree algorithm I found online

I am trying to learn decision trees but it has been difficult because the examples are extremely long and tedious and everybody seems to have a different algorithm in mind After some digging I found ...
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2 answers
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Identify the confidence of the impact of a proposed improvement initiative

Assume I have a manufacturing process that involves a moving train. It has failures of certain types like brakes and steering and also the weather. However, we can not do anything with regard to ...
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How do decision trees decide the value to be split upon for continous variables? [duplicate]

I know that decision trees make the split based on some metric such as entropy, information gain, gini index etc. But for continous variables how does it figure the value at which to make a split. For ...
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Best approach to predict the days to wait to buy an airline ticket

I have a dataset with flight price observations collected over 3 months. I am trying to make a model that is able to recommend to a traveler the best time to buy a flight ticket, telling him the days ...
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Would I get optimistic results if I use random forest twice on my training set?

I have a data with 209k instances with 32 features and 3 unbalanced target labels (0,1,2). I am planning to apply Random Forest classifier since I also want to have insights about importance of the ...
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Are decision trees in SPSS classification or clustering trees?

I have a dataset consisting of 140 features (categorical and continuous variables) and one dependent variable (continuous). I used SPSS classify function without setting any classes, using CHAID ...
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How does a regression tree split the y variable

How does a regression tree split the y variable? Is it just a case of even chunking of the range or are the chunks of variable size?
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How to decide what is a "good move" or a "bad move" with supervised learning on a game?

Let's take an example game, like connect $4$, which is nice and simple. I know minimax is a super easy algorithm to program here, but let's say instead I wanted to create a Machine Learning model for ...
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1 answer
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Unbalanced dataset classification problem

I have a binary classification problem and I'm working with an unbalanced dataset. The count for each class in the training set looks like: ...
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1 answer
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What is the impact of a dummy variables to boosted trees?

I am currently reading the book "Random Forests" by Yu. L. Pavlov. Then it came across my mind the question If I were to use ensembled tree, say XGBOOST, do I need to transform each ...
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Is a "decision boundary" incompatible with proper scoring rules?

Having a decision boundary in a binary classification problem tells me that if the point lies on one side of the boundary, classify as $0$; if the point lies on the other side, classify as $1$. What ...
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How to convert continuous variable dataset into binary discrete values using Chi-Square testing for decision making

I have a dataset that contains continuous values for an attribute ranging from 0 - 100. I want to convert these continuous variables into two discreet values (say Label L1 and Label L2), So that the ...
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3 votes
1 answer
121 views

Overview of the main methods to prune decision trees

Could someone explain the main pruning techniques for decision trees. So something like the 3 most common techniques with a short explanation of how they work. I have looked online but this, ...
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1 answer
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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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10 votes
1 answer
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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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4 votes
1 answer
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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 ...
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3 votes
1 answer
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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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1 answer
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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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1 vote
1 answer
181 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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1 vote
1 answer
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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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1 answer
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Interpretation of regression tree with Poisson data

Above is a decision tree made by following code ...
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2 votes
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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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0 votes
2 answers
2k 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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1 answer
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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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1 vote
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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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1 answer
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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, ...
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4 votes
1 answer
80 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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2 votes
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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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0 votes
1 answer
34 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 ...
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2 votes
2 answers
164 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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1 answer
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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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How does decision_function score values help calculate thresholds and in turn precision recall curves?

...
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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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19 votes
1 answer
26k 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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1 vote
1 answer
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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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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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