Questions tagged [confusion-matrix]
A confusion matrix is a special contingency table used to evaluate the predictive accuracy of a classifier. Predicted classes are listed in rows and actual classes in columns, with counts of respective cases in each cell.
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Finding Accuracy, Recall, Precision, and F1 from Matlab Confusion Matrix
I'm working on a project to find the highest accuracy between KNN and a Decision Tree for Classification using Matlab.
How to calculate the Accuracy, Recall, Precision, and F1 from the output below? ...
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Does a confusion matrix make sense for extraction tasks?
I'm extracting company names from text blobs.
I'm trying to calculate recall.
Confusion matrices makes more sense to me from a binary classification point of view, ...
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How to calculate the training accuracy of a decision tree?
The hint given was to construct a confusion matrix.
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warning 'newdata' had X row but variables found have Y rows
Linear Discriminant Analysis (LDA)+logistic regression model
lda_model <- lda(train_labels ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width, data = train_data)
LDA scores for the training ...
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How to improve accuracy on a single class out of 3 classes in model
I am training a classification model with 3 classes using a deep neural network.
The classes have been resampled and balanced.
I have around 600000 samples... equally distributed.
The dataset is also ...
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How many Non-frauds would be blocked to stop one Fraud based on the given confusion matrix?
One of the questions in a recent online test that I couldn't answer is below(I couldn't copy paste exact question, so this one is totally from memory. Kindly excuse in case of poor wording.)
We have a ...
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Recall and Precision ML models
I use decision trees for a binary classification. To evaluate the model, I use K-fold cross-validation, where k = 10.
When I run the model n times, I get a relatively constant accuracy across all ...
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9-dimensional data classification
I have a dataset with 9 columns and 20 million rows. I have now split the dataset into two parts using IQR.
One part is my inliers, the other part is my outliers.
I now want to build a categorizer ...
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One class confusion matrix notation for model evaluation
A one class classification set-up for a set of rules acting as a model, where
each input is a whole dataset
model makes some decision within the dataset for each entry
output is decisions made for ...
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How to read confusion matrix from multiclass dataset?
I have a dataset with multi class for classification. After train and test, tried to plot with confusion matrix. And I found it really different with dataset with simple label true false or yes no. So ...
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why my accuracy and recall become higher in testing than training. How to interpret
Why in hidden layer 2 and 3 in neural networks scratch, the accuracy and recall I got low, but in testing the accuracy and recall become higher. In hidden layer 4 it's get weird when sampling strategy ...
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Some simple questions about confusion matrix and metrics in general
I will first tell you about the context then ask my questions.
The model detects hate speech and the training and testing datasets are imbalanced (NLP).
My questions:
Is this considered a good model?
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How does the background class work in object detection?
I am using YOLOv5 for object detection.
I understand that any labelled classes that are not predicted, that is, false negatives (FN) shows up as background. But how are the false positive (FP) being ...
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High Recall and Low Precision for Binary CNN model
I was training a CNN model for binary classification. The training and validation accuracy seemed good. However, the precision is low and the recall is high (High false positive).
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What are valid measures for reporting k-fold score in the case of confusion-matrix?
I know when model is made to predict a float value, a common approach to report the models validation is using k-fold technique and calculating the average of all folds accuracy (here is a similar ...
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Precision, recall and importance of them in the imbalance problem
I have a test dataset. The dataset is an imbalanced dataset. The total training instances for the dataset is 543 among them minority class(yes) is 75 and the majority class(No) is 468. The class of ...
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Error rate of a class from confusion matrix
My professor gives a multiclass confusion matrix and asks for the error rate of a certain class. Unfortunately, the professor refuses to give a definition.
I think the closest value to an error rate ...
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What mean a column in zero in confusion matrix?
When training my model and reviewing the confusion matrix, there are completely zero columns for each specific category, what does this mean, is there an error or how do I interpret it?
I use the ...
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Azure AutoML: decimal numbers in confusion matrix
I have been running Azure AutoML on a binary classification task. When I visualize the performance and the confusion matrix in particular, I observe the following:
How can I have decimal numbers in ...
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how to reduce overfitting and improve confusion matrix
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 ...
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How to add class labels to confusion matrix of multi class classification
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',...
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How can I use a confusion matrix in image captioning?
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 ...
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Deep Learning accuracy vs Confusion Matrix accuracy
I am working on deep learning with fer2013 dataset.
After training the model I got val_precision: 0.9168 (precision: 0.8492)
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specificity for 3 class
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-...
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How to balance sensitivity(sn) and specificity(sp) of an Artificial Neural Network model?
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 ...
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Difficulties in create a confusion matrix in R for Yes or No
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 ...
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Logistic Regression test accuracy vs deployment
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, ...
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Discrepancy between cross-validation and un-seen data 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.
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What happens to auc when true positive rate grows
How does change in true positive rate affects AUC? Does increase of TPR lead to increase of AUC as well?
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Plotting confusion matrix for multi classification problem
I am using google colab to solve a multi-classification problem. I am trying to plot the confusion matrix for this problem, I have tried doing so using :
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Can you use a Confusion Matrix for a image detection problem?
I have read the classic examples of using a Confusion Matrix for a classification problem. ("Does a patient have cancer, or not?)
The ML system I am working with is for detecting objects in an ...
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Confusion matrix - one class with zero values
I'm applying the Random Forest classifier on a dataset with 645 records and 12 features (selected by KBest method). This dataset contains a class with 4 possible values (1..4). When I plot the ...
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Should I consider unreadable records in confusion matrix while calculating accuracy?
I have 6 classes in my dataset and model. Dataset is regarding ECG signal
Having x number of records for each of these classes.
The confusion matrix looks like this -
My question is, should I ...
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In multi-class, is the average accuracy of each class in confusion metrics equal to the accuracy calculated from cross validation?
When I calculate accuracy through cross-validation, it gives me a different accuracy than when I calculate through confusion metrics. Why does it give different accuracy? Is overall calculated ...
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Imbalanced classification task – Discrepancy between learning curves and test set evaluation
I have a binary classification task related to customer churn for a bank. The dataset contains 10,000 instances and 11 features. The target variable is imbalanced (80% remained as customers (0), 20% ...
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How to track loss and accuracy in PyTorch?
I have made model and it is working fine for the MNIST dataset but further in the assignment it says to track loss and accuracy of the model, which I do not know how to do it. I have also written some ...
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Confusion matrix with different levels
I want to print a confusion matrix, but data and reference have not the same level. How can I do?
this is my actual code:
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Is it possible to combine two confusion matrices?
Assume I have two different algorithms that tests whether a given image contains a goat or not. I apply these two algorithms to two different datasets and obtain two confusion matrices.
Now I want to ...
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How to create a confusion matrix for k-means with two features?
I have the need to do a confusion matrix for data run through k-means with two features. I am aware that this is a clustering algorithm and not a classification algorithm but I have seen some ...
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How do I calculate precision, recall, specificity, sensitivity manually?
I have actual class labels and predicted class labels:
...
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Confusion matrix terminology
I am working on machine learning with a supervised problem with 2 classes: NO and YES, and I need some precision about confusion matrix. I read 2 differents terminologies, some writes matrix confusion ...
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How are scores calculated for each class of binary classification
The formula for Precision is TP / TP + FP, but how to apply it individually for each class of a binary classification problem,
For example here the precision, recall and f1 scores are calculated for ...
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Comparison of classifier confusion matrices
I tried implementing Logistic regression, Linear Discriminant Analysis and KNN for the smarket dataset provided in "An Introduction to Statistical Learning" in python.
Logistic Regression ...
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Confusion matrix of 3*2
I would like to include confusion matrix in my research report. I have a binary classification problem. The positive class is further divided into two types for example: Real Positive and Obstruction ...
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Unable to generate confusion matrix
I am using keras flow from directory for image segmentation. Following are my codes
...
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Recall/Precision Metrics on Azure AutoML seem to be oriented to majority class, and I'm trying to focus on minority class
I am running some experiments in Azure using AutoML. My problem is a binary classification one, with highly imbalanced classes (basically trying to predict what factors make a deal "WON" ...
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Confusion matrix and accuracy not in sync
I am getting the following result for the confusion matrix and accuracy for a logistic
regression model.
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Viewing false positive rows in python
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 ...
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How is there an inverse relation between precision and recall?
What I know?
Firstly,
Precision= $\frac{TP}{TP+FP}$
Recall=$\frac{TP}{TP+FN}$
What book says?
A model that declares every record has high recall but low precision.
I understand that if predicted ...
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