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Feature Importance for Regression Random Forest
Hello Everyone, I am looking for an operator (or any other way) to find the attribute importance of my model. I have selected an RF model and tried to use the operator "Weight by Tree Importance" to find the weights of my attributes. However, I received the following error message: Attribute Weights cannot be extracted…
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Most important features
Hello again. I would like to know if there is an operator that shows the most important features. I am using a dataset which consists of 1096 columns and 96000 rows including clinical and laboratory records. Βy knowing the most important features I will be able to evaluate and process the attributes that have an important…
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Feature weight by Relief
Hi, I am a beginner at RapidMiner and unfamiliar with it. I saw that RapidMiner offers feature selection by the node "Feature Weight by Relief". However, I would like to know which Relief algorithm does the node implement, as there are a few such as Relief, ReliefF and RReliefF. The node requires the parameters…
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Time series data with binary column analysis
Hello there , i m trying to generate two timestamp columns (from,to) out of the table given below in such a way that whenever there is '1' after '0' in the resultant column that particular timestamp should copied to the from column and whenever there is '0' after '1' that particular timestamp should be copied to the "to"…
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Feature selection: methods depending on data types
Dear fellow Rapidminers, I am trying to predict a binary dependent outcome of a large (80.000 obs.) dataset with 210 possible predictors. Before attempting any backward elimination or maybe even brute force methods I would like to identify the most useful variables to reduce computational time. The variables are both…
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Auto Model: Performance is worse when auto feature selection / generation turned on?
Hello, I am new to the machine learning world am self teaching myself by playing around with rapid miner studio. I have just noticed something that doesn't seem to make sense to me and am hoping someone could explain it to me. I put the same data set in auto model and at first ran it with 'automatic feature selection /…
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How can I take only the variables with at least 5.000 observations?
Hello folks, I need a hand here... How can I take only the variables with at least 5.000 observations? I have too many variables, thank
you in advance. Cecilia
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How to reproduce Principal Components to original features?
Dear Community, is there a possibility in RapidMiner to see which of the original features were selected/transformed to the extracted Principal Components? Thank you in advance for your help! Best regards! Fatih
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#ASK Pseudocode Feature Weighting Using PSO
Anyone have a pseudocode about feature weighting based PSO (OPTIMIZE WEIGHT PSO) ? Please, Share It. thank you.
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How to explain the binary2multiclass
Hello good day! I am using a polynominal by binomial operator inside of it is the logistic regression now the result of the model is like this and I really dont know how to interpret or just explain the result: I believe I don't need to upload the dataset. I know that Positive
coefficient make the event more likely and…
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Linear regression operator - 'greedy' feature selection option
Can someone please help with more details on how the "greedy" feature selection in Linear Regression operator works? In the Optimize Selection operator, the two greedy algorithms (forward selection and backward elimination) are clearly specified. However, in the case of the Linear Regression operator, it is not yet clear…
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Feature Request: In feature selection operators, allow specifying features to always include
It would be useful for the feature selection operators (Forward Selection, Backward Elimination, Optimize Selection, Optimize Selection (Evolutionary), etc) to have the ability to provide a starting set of features that we always want to include. This would be useful when we already have a working model with a basic set of…
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Regression issue
Hi everybody I am using linear regression operatore. everything is fine except this issue. I have 15 attributes for linear regression. I use feature selection M5 prime but the final model is weird. I receive "?" instead of numbers in the p-value column. when I remove one attribute form the data, the final result doesn't…
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Optimize Selection Weights
I am using the Optimize Selection(Evolutionary) and Logistic Regression in order to extract predictive features of Students NAT performance. My input features are Student Subject Grades and my Output Feature is the National Achievement Test Score. The problem is the result weight in my Optimize Selection Outputs 1 and 0,…
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Automodel and variations in feature weights and ranking
Questions: * If the weight of a feature dramatically changes depending on the model used, the ranking of the 5 most important features are varying a lot between the models. Because these features have a context related to a patient population, and we believe that time onset to ER is very important, we really were looking…
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Error: Only One Label
Currently working on an Educational Data Mining Project. I got a very common problem to some of my data sets I cant search this problem anywhere. Whenever I run my the process it always states 'Only one Label', The learning scheme Logistic regression does not sufficient capabilities for handling an example set with only…
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Feature selection - maximize recall performance
Hello, I'm a bit out in the blue on this one. How is it possible to maximize the recall performance metric in the feature selection phase with the Automate Feature Selection operator? Normally when I use this operator I minimized the classification error metric and then it don't generate any errors. Though when I try with…
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Hello! Does anybody know if RapidMiner has an extension for Concept Linking?
Hi! I am workin on a text mining project on RapidMiner and I was wondering if RapidMiner has the capability to do Concept Linking or something similar.
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Cross-validation Features
Hello, I am currently performing cross-validation (CV), and within this process, "Forward Selection" is performed during training. How can I output the chosen features once CV has completed? I've tried countless solutions including using the "Weights to Data" and "Data to Weights" operators, but neither of these output the…
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Why does RapidMiner delete datarows when automatic feature selection is applied?
Maybe a very stupid question, but my input consists of 15577 data rows, my output only consists of 4500 data rows when I apply auto feature selection in data preparation. In addition to that, can I reliably compare the confusion matrices of the baseline model (with 15577 rows) and the RapidMiner model (with +/- 4500 rows)…
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An idea about the feature engineering strategy inside AutoModel
Dear all, Firsly, please consider this thread as an idea, a debate and not a "feature request". Thinking about a recent thread involving feature selection inside AutoModel, an idea crossed my mind...As RM ambassador, I will now share this idea : The following strategy applies when the user considers several models inside…
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Classifiation SubProcess for Feature Selection
Hello together, do you have a recommendation with regard to the question of which classification model sould be used within Feature Selection (e.g. Optimize Selection or Backward Elimination) to be able to efficiently select attributes or rather dimensions based on a high-dimensional TF-IDF matrix? Thank you in advance for…
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How to find the most important features in a dataset?
I have a dataset in csv format with more than 500 columns, I have imported it to a database marking every column as polynomial since they all hold different types of information and now, I want to find which of those are the most important. So far, I have managed to get a table with the feature and its weight, using the…
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Enhancement of Running Time - Forward Elimination
Dear Community, I have a question with regard to the ForwadElimination subprocess. Which classification model would you use within crossvalidation in order to enhance the running time? I am currently conducting a Forwald Elimination based on matrix with 72.000 rows and 9000 attributes. I've chosen SVM as classifier for my…
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How does Feature Selection - Forward Elimination work in detail?
Hello together, I have a question regarding regarding the picking process of Forward Elimination. The documentation of RapidMiner tells us that: "The Forward Selection operator starts with an empty selection of attributes and, in each round, it adds each unused attribute of the given ExampleSet. For each added attribute,…