I am trying to apply the following model on my data which is consists of (4030 samples as 5 classes) each sample is MFCC features which is extracted from an audio clip consisting of (20 second) and I am trying to apply classification, but I got very poor accuracy and I also have overfitting, , Although I am using data augmentation and I also try to apply Batch Normalization to improve overfitting but the result is very bad. the Model: …
How do I add class labels to the confusion matrix? The label display number in the label not the actual value of the label Eg. labels = ['A','B','C','D','E','F','G','H','I','J','K','L','M','N','O','P','Q','R','S','T','U','V','W','X','Y','Z'] Here is the code I used to generate it. x_train, y_train, x_test, y_test = train_images, train_labels, test_images, test_labels model = KNeighborsClassifier(n_neighbors=7, metric='euclidean') model.fit(x_train, y_train) # predict labels for test data predictions = model.predict(x_test) # Print overall accuracy print("KNN Accuracy = ", metrics.accuracy_score(y_test, predictions)) # Print confusion matrix cm = confusion_matrix(y_test, predictions) plt.subplots(figsize=(30, …
I read that a confusion matrix is used with image classification but if I need to draw it with image captioning how to use it or can I draw it in the evaluation model phase for example if yes how can I start?
I was reading an answer in qoura to calculate the specificity of a 3 class classifier from a confusion matrix. In the below answer https://www.quora.com/How-do-I-get-specificity-and-sensitivity-from-a-three-classes-confusion-matrix For below 3-class confusion matrix, The below is a screenshot from the answer. the sensitivity and specificity would be found by calculating the following: My question is in numerator for specificity which is false negatives shouldnt it be 4 terms. Eg if we are calculating w/r 1, Then in the table n22,n33,n32 and n23 were …
I understand building a ROC curve when the output is a probability, say, from a logistic regression model. You can build a ROC curve by varying the cutoff threshold. But what about decision trees of the form: if attribute_1 > x: decision = positive else: if attribute_2 < y: decision = position else: decision = negative You can adjust the cutoff for both attributes and all will affect your confusion matrix. Does it make sense to build a ROC curves …
I have been working on a binary classification problem of protein sequences. I have used a feed-forward neural network with two hidden layers. I have the training and validation accuracy/loss curves that the model has trained pretty well without overfitting/underfitting. Then, while testing on independent dataset, I have following results: Accuracy: 0.7672583826429981, MCC: 0.5401163645598229, Sensitivity: 0.8379446640316206, Specificity: 0.6968503937007874, Confusion matrix: [[177 77] [ 41 212]] The results are already pretty impressive for this particular problem in terms of Accuracy and …
I have a classification problem and I am producing a confusion matrix. Ideally one wants to get all results in the diagonal. I get quite many points around diagonal for different algorithms. Still for my use-case I want to favor algorithms that underpredict the class (I have ordinal data) and not overpredict. Is there a metric that can measure under and overprediction and rate those errors with a different weight? The typical accuracy, precision terms assume that all mistakes are …
I am new to regression and confusion matrix and trying to create a confusion matrix from logistic binary regression model. I am trying to create a confusion matrix from Yes or No values from the column, Survived. I am using a default dataset, Titanic. I received an error when trying to perform Confusion Matrix The dataset, Titanic content is found here. Titanic Content Here is the R code below. example$Class<- as.factor(example$Class) example$Sex<- as.factor(example$Sex) example$Age<- as.factor(example$Age) example$Survived<- as.factor(example$Survived) trainRowNum <- createDataPartition(example$Survived, …
I’m comparing two confusion matrices: https://en.wikipedia.org/wiki/Confusion_matrix#Table_of_confusion https://en.wikipedia.org/wiki/Type_I_and_type_II_errors The 2nd is rotated, the Decision is on Y-axis. But I assume both reflect the same concept. I have two options to render the word “Reject”. (1) When we look at Null hypothesis matrix, the Reject of a “True Null hypothesis” means a decision which doesn’t reflect reality (convicting an innocent), and this is indeed FP (type I). (2) Following Confusion_matrix wiki, I interpret Reject as False. Therefore, making a False decision (H0 …
I have a confusion matrix TN= 27 FP=20 FN =11 TP=6 I want to calculate the weighted average for accuracy, sensitivity and specificity. I know the equation but unsure how to do the weighted averages.
I am working on a three class problem. How do you calculate precision, recall, f-score, and MCC for each class while using MATLAB? Here is my confusion matrix: 2775 0 0 1 591 0 4 0 845 I am calculating Accuracy from the Confusion matrix in this way: Accuracyy = 100*sum(diag(confusionMat))./sum(confusionMat(:)); I would like to measure the below performance measures for each class. I know the formulas, but not how to execute this in MATLAB. Please help. I would really …
I got values for the confusion matrix using: tn, fp, fn, tp = confusion_matrix(y_true, y_pred).ravel() 10000 13000 500 1500 Now, I wish to see what data is in the each tn, fp, fn, and tp. I have tried various options, but the kernel keeps on dying. I am working with Python3 on Jupyter Notebook. X_train, X_test, y_train, y_test = train_test_split( X, y, train_size=5000, random_state=1) X_train.shape, y_train.shape, X_test.shape, y_test.shape ((5000, 21), (5000, 1), (25000, 21), (25000, 1)) y_pred = pd.Series(clf_iforest.predict(X_test)) For …
I am unable to infer anything about the model from the following confusion matrix. What is the color coding actually specifying? For example, when predicted label is 1 and true label is 1, the value in the matrix at that point is 0.20. Does that mean its accuracy? Does it mean that the model is only able to predict 1 only 20% of times when the label is actually 1? PS: SO URL for a more clear image
I am working on a problem where I make some weekly predictions. I gathered the data myself and did some pre-processing steps and I end up with 6 features. I split the dataset 60-20-20 in train, holdout, test sets and I train a Logistic Regression model. I get very good holdout and test accuracy (>95%) with only a few FP, FN. However, when making predictions for new data in the end the results are nowhere near there. I make predictions …
I am facing an issue with an imbalanced dataset. The dataset contains 20% targets and 80% non-targets. I am expecting a confusion matrix below when I give un-seen data to the trained model. [[1200 0 ] [0 240]] In reality I am getting a confusion matrix below. As you must have observed, it is classifying very less targets. [[1133 67] [ 227 13]] The training and validation curve of a CNN model looks like below. Any thoughts on, why so …