Questions tagged [weka]
Weka (Waikato Environment for Knowledge Analysis) is a collection of machine learning algorithms for data mining tasks.
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Information Gain Ratio
I tried ranking some variables using WEKA cross-validated information gain ratio. Apart from five of the variables, the other variable average merit came back as 0. is that normal?
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How to handle missing values NaiveBayes Scikit Learn
I am working with a dataset which has 34 features (numerical, nominal) and the target class. Several of the columns have missing values, especially one column has approximately 50% missing values.
I ...
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What is TN rate in Weka results?
I have a question about classifications model in Weka.
Which parameters in the result from weka is the TN rate(specificity)?
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How do you interpret the matrix confusion in this Naïve Bayes output?
Why are my correctly classified instances lower than incorrectly classified?
This was tested using Naive Bayes with option testing Cross-Validation set at 10 folds.
Here is the image of the results: ...
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Is it important to know the variance of correctly classified instances in cross-validation?
To create a classification model for the Iris data set, I used the J48 (C4.5) algorithm in Weka. To evaluate the model, I used 10-fold cross-validation. These are the cross-validation results:
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Why is there a ZeroR and OneR classifier but no TwoR classifier?
I am using WEKA, and I noticed that there is a ZeroR classifier and a OneR classifier. The ZeroR classifier always predicts the majority class, while the OneR classifier bases its predictions on only ...
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Numeric variable with outliers as a categories
I'm working with a dataset that has a few variables that I'm having difficulty trying to preprocess. So one of them is called MENTHLTH where it is a numeric variable.
The point of the variable is to ...
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Difference between One Rule Classifier and Decision Stump in WEKA
WEKA Explorer seems to come up with two different models for OneR (rules) and Decision stump (trees). Is has to be the underlying measure of "best split" that is different. But for a single ...
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Cross-validation giving better results than separate test test
I am using J48 in Weka.
The accuracy results of the cross-validation is about 99.9% but when I provide a separate test set the accuracy drops to about 65% or less.
So the question is why there is a ...
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Calculating three average precisions and a single value for ROC from raw predicted class outputs
I'm not a statistician or mathematician so I apologize if I use any terms incorrectly. Please do point out any errors in my use of terminology.
The four values I need are the equivalent of Weka's ROC ...
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Calculating Specificity from Weka output
In short I need to know or calculate the sensitivity and specificity from Weka 3.8.4 output.
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Leave-One-Out CrossValidation with Weka
I have a dataset consisting of 5 subjects (5 different virus names and 5 benign program names). The programs belong to the class "benign" and the viruses to the class "infected". There are 1000 ...
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Interpret the clustering results of Weka to measure the performance [closed]
I'm having the Boston dataset, where it's class variable in the housing price. So I think regression is more suitable for this dataset, so we can predictions.
I'm using Weka for this. I used several ...
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How to create a test set in stacking when doing cross validation
I am using Weka to implement stacking with k-fold cross validation. As I understand, we first divide our dataset in to k folds, then we use k-1 folds for training and 1 fold for testing. This ...
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Cross validation or percentage split
I got a data-set with 50 different classes. Around 40000 instances and 48 features(attributes), features are statistical values. I am using weka tool to train and test a model that can perform ...
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Can we apply SMOTE on data with k-fold CV
The SMOTE for the imbalance should be applied for the training data only, right? Can we still do it (perform SMOTE on training data) while we select the k-fold CV and does not go for splitting the ...
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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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How to binarize data for FP Growth in Weka?
I have a CSV file where all comlumns contains numerical values except for the quality column which contains nominal values. I want to use FP Growth Weka algorithm ...
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K-means calculate MSE in Weka
I am doing some clustering analysis with Weka and decided to apply the k-means algorithm (the clusterer SimpleKMeans).
On my first analysis I ran the algorithm with 2 clusters.
Then, after finding ...
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Meaning of confidence factor in J48
I try to use J48 classifier from RWeka library in R (C4.5 algorithm). I can parametrize this classifier with C parameter which means 'confidence factor'. What does this value exactly mean? I know that ...
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SVM and correlation
Can anyone guide me about the feature selection based on correlation using SVM? RBF kernel check the correlation too or not? I am using weka and matlab. Any help would be appreciated.
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How to deal with a negative Kappa in classification?
I have a dataset with one binary class to be predicted, with 18 binary predictors and 17400 rows.
Here I used a stratified split, with approximately 85% (14648 rows) for training and 15% (2752) for ...
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TF-IDF String to Vector Weka bias
For example, let's say I have a text dataset like:
"words text etc",label
"words text etc",label
"words text etc",label
If I ...
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What is the meaning of AUC being high when accuracy is not? [duplicate]
I'm testing several classifiers in Weka Experimenter.
Some of them have — at the same time — low accuracy (Percent_correct statistic) and high AUC.
How should the quality of such ...
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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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Performance measures in Predictive models
I developed predictive models and I reported G-mean ,F-measure, Sensitivity, and Specificity which are very low. i wonder if anyone has a reference for the threshold of these measures ? I want to ...
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WEKA Visualization: getting class percentages
I've just started out trying on ML.
In WEKA, when I try to visualize a data set I find it hard to tell the class ratio for a certain nominal attribute value due to the differing amount of instances, ...
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What is a good percentage value for SMOTE in Weka
I am using the oversampling technique named as SMOTE to balance my dataset in Weka. To balance my dataset I needed to use 1300 as my -p (percentage) value. The default -p (percentage) value is 100.
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Standardize a sample dataset
I created a model with J48. Before creating the model, data were standardized. Now I want to test this model with a sample dataset. Before applying data to the model, I believe data should be ...
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Random forest parameters
I'm trying to make decisions regarding Random forest parameters for classification. My dataset contains 26 features and 6300 instances. How can I decide the values of (the number of trees, number of ...
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Interpreting results from WEKA
I am trying my to built a model that predicts whether or not customers will churn, using a dataset with 7000 instances (rows) and 20 features.
I am using WEKA and experimenting with a J48 Decision ...
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How to tweak a Weka model? [closed]
I know how to run a Weka model, but I'd like to tune some parameters. How do I do that?
Thanks,
a newb
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Should classes be balanced before or after splitting into sets?
I've split my data and performed pre-processing. I ran some basic classifiers on it and got accuracies within 70-80%, which to me seems fairly low. One thing I didn't do was balance my classes before ...
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Log-Likelihood in EM Cluster
I am doing clustering in Weka for a school project. I am trying to compare two Weka outputs with log-likelihood:
Number of clusters selected by cross validation: 6
...
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Cross-validation with unbalanced-classes
I'm a little confused on how to manage my data set with WEKA.(for data mining)
I have a Dat set including 11377 record classified as follows:
11111 records have class YES
266 records have class NO
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872
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Negative feature value
I use a logistic regression model for reranking some documents where a normalized features of some candidates may have negative real value so that its predicted value may get lower score(low ...
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How to interpret coefficients of nominal independent variables in Weka?
I'm struggling a bit with interpreting the output of a linear regression in Weka. This is my model:
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Weka - only care about "top" instances
I'm trying to classify instances between 2 classes ("good" and "bad"). My ultimate goal is to be able to predict good instances, but I don't need to identify all good instances. For example, say I ...
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I need to see the incidences in my data set in WEKA
I have a dataset that contains 44804 instances, each with the attributes: diagnoses, age and quantities (same diagnoses includes). I want to see which diagnosis is most seen for a given age.
For ...
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142
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Is value of correlation matrix enough criteria to delete an attibute?
I need to do some clustering with my data set. I have 200 attributes and 18 tuples only. So I am trying to do some data cleaning. I deleted all attributes that has 0 as data and reached till 165. Now ...
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guide for text classification using weka
I have a set of 2000 small texts (each less than 500 words) that I manually categorized. All the texts are in the same main subject, and I want to separate them into distinct groups based on their ...
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MultiBoost vs Gradient Boosted Decision Trees
Why isn't there an implementation of GBDT in Weka? (Java ML library)
Instead the recommendation is to use the MultiBoost algorithm with J48 (Java implementation of Decision Trees - C4.5 algorithm). ...
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Decision tree: Perfect classification with (a dicotomic) class noise at 100%, but almost null prediction with noise at 99%. (Tried 2 alg in R). Why?
I am using a dataset with a dicotomic class and testing how noise affects the decision tree j48 - from Rweka, using R - performance. I´m adding noise, and using confindence factors from 0.01 to 0.5 ...
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Weka clustering methods greyed out
I generated a csv file with 167 attributes and around 5000 entries. One is a nominal attribute, two are dates and the rest is numerical.
I can import the file into the weka explorer without problems. ...
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How does a decision tree split on a categorical variable? [duplicate]
Some implementations of decision trees (eg cran/tree) can split on categorical variables where the split separates the variable into 2 groups:
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553
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Goodness-of-fit vs maximum likelihood for logistic regression?
From what I understand, maximum likelihood is used to estimate a parameter alpha in a way that maximizes the probability P(Y=|x,alpha) for example. It is used for logistic regression in order to get ...
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How to predict the error accuracy using these values from weka and how to intepret the output?
Classifier Model
Linear Regression Model
bug =
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J48 decision trees in weka
I am using J48 decision tree classifier in weka. In the testing option I am using percentage split as my preferred method. The split use is 70% train and 30% test. My understanding is that when I use ...
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3k
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How do weka classifiers deal with missing values? [closed]
I tried using a training set that has missing values. I applied filters (like replace missing data) and then after there were no more missing data I applied naive bayes, trees etc... I thought this ...
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3k
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Classification and mixed categorical and numeric variables
I've been working a little with weka and so far I haven't made my own database to apply a classifier but I've tried to look at the already existing files and from what I've seen there is absolutely no ...