machine-learning,nlp,scikit-learn,svm,confusion-matrix

Classification report must be straightforward - a report of P/R/F-Measure for each element in your test data. In Multiclass problems, it is not a good idea to read Precision/Recall and F-Measure over the whole data any imbalance would make you feel you've reached better results. That's where such reports help....

In a nutshell, you have some tags where that particular tag was never guessed. Because you're normalizing by the number of times the tag was guessed, you have a row of 0/0 which yields np.nan. By default, matplotlib's colorbars will set NaN's to have no fill color, causing the background...

You are trying to get an idea of the in sample fit using a confusion matrix. Your first approach using the glm() function is fine. The problem with the second approach using train() lies in the returned object. You are trying to extract the in sample fitted values from it...

Given a confusion matrix cm, the overall accuracy is obtained by overall.accuracy <- cm$overall['Accuracy'] It's the first time I see the caret package, so how did I know this? Since you didn't provide an example, I searched for an example code for caret confusion matrices. Here it is (I only...

java,confusion-matrix,statistical-test

if every variable is double your code produces the desired output 1.0 : just compile and run the following java class which assigns the values you want to be assigned to trueP, trueN, falseP and falseN: class confusion { public static void main(String[] args) { double trueP=6930; double trueN=6924; double...

python,scikit-learn,confusion-matrix

I think that 0.695652 is the same thing with 0.70. In the scikit-learn f1_score documentation explains that in default mode : F1 score gives the positive class in binary classification. Also you can easily reach the score of 0.86 with the formulation of F1 score. The formulation of F1 score...

Error computation The lines index = cellfun(@strcmp,y,labels(test)); errorMat(i) = sum(index)/length(y); computes the success rate of the i-th classification (between 0 and 1). The average success rate is then the mean of all the 10 success rates (one for each evaluation). The line cvError = 1-mean(errorMat); is then the average error...

javascript,html,google-visualization,data-visualization,confusion-matrix

I would use a custom tooltip and render your confusion matrices in a column of the DataTable. https://developers.google.com/chart/interactive/docs/customizing_tooltip_content Here's an interactive demo with something like what you might like. Note that after converting the array to a data table I add role and html properties to the tooltip column. <!DOCTYPE...

machine-learning,confusion-matrix,orange

Confusion matrix and ROC analysis are widgets intended to analyze the results of the classification that come from a Test Learners widget. A typical schema for such evaluation is: Widgets for clustering can add a column with cluster labels to the data set, but there is no widget to turn...

matlab,indexing,confusion-matrix

This error tells me that you've defined a variable max somewhere in your code. Indexing cannot yield multiple results Why? Because otherwise [Max, argmax1]= max(simoutelem); wouldn't be taken as a case of "Indexing". Easy proof at command line: [a b] = max([1 2 3 4 5]) % works max =...

r,random-forest,confusion-matrix

Use randomForest(..., do.trace=T) to see the OOB error during training, by both class and ntree. (FYI you chose ntree=1 so you'll only get just one rpart tree, not a forest, this kind of defeats the purpose of using RF, and randomly choosing a subset of both features and samples. You...

You have total 8 classes: a, b, c, d, e, f, g, h. You will thus get 8 different TP, FP, FN, and TN numbers. For instance, in the case of a class, TP (instance belongs to a, classified as a) = 1086 FP (instance belongs to others, classified as...

python,numpy,machine-learning,scikit-learn,confusion-matrix

In your line: cm_t = confusion_matrix(y_test[228:317,t[228:317]) you are missing a bracket. It should be: cm_t = confusion_matrix(y_test[228:317],t[228:317]) ...

c++,opencv,confusion-matrix,dlib

Seems like I was right with my first guess. In the original you normalize like this: confMat[i][m] / exCount[m] * 100 while in the correct code you normalize like this: confMat[m][i] / exCount[m] * 100 Depending on whether exCount is counting the totals per row or per column you only...

You can try data$Classifier <- (rowSums(data[,1:3]) >=2)+0L data # A B C Actual Classifier #d1 1 1 1 1 1 #d2 0 0 1 0 0 #d3 1 1 0 0 1 #d4 0 0 0 0 0 #d5 0 1 1 1 1 #d6 1 0 1 1 1...