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   • Confusion Matrix in Machine Learning

     In machine learning, classification is the process of categorizing a given set of data into different categories. In machine learning, to measure the performance of the classification model, we use the confusion matrix. Through this tutorial, understand the significance of the confusion matrix.

     What is a Confusion Matrix?

     A confusion matrix is a matrix that summarizes the performance of a machine learning model on a set of test data. It is a means of displaying the number of accurate and inaccurate instances based on the model’s predictions. It is often used to measure the performance of classification models, which aim to predict a categorical label for each input instance.

     The matrix displays the number of instances produced by the model on the test data.

     • True positives (TP): occur when the model accurately predicts a positive data point.
     • True negatives (TN): occur when the model accurately predicts a negative data point.

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   • Confusion Matrix in Machine Learning

     In machine Learning, Classification is the process of categorizing a given set of data into different categories. In Machine Learning, To measure the performance of the classification model we use the confusion matrix.

     Confusion Matrix

     A confusion matrix is a matrix that summarizes the performance of a machine learning model on a set of test data. It is often used to measure the performance of classification models, which aim to predict a categorical label for each input instance. The matrix displays the number of true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN) produced by the model on the test data.

     For binary classification, the matrix will be of a 2X2 table, For multi-class classification, the matrix shape will be equal to the number of classes i.e for n classes it will be nXn. 

     A 2X2 Confusion matrix is shown below for the image recognization having a Dog image or Not Dog image.

      

     Actual

     Dog

     Not Dog

     Predicted

     Dog

     True Positive (TP)

     False Positive (FP)

     Not Dog

     False Negative (FN)

     True Negative (TN)

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   • Machine Learning: Getting error in Confusion Matrix

     Top Answer

     Answered Nov 01, 2018 · 1 votes

     To give a simple answer : A confusion matrix is a table that is often used to describe the performance of a classification model (or "classifier") . In your case you are trying to find the confusion matrix for the continuous variables. for regression use mean square error. Have a look at this lecture pdf: http://www.it.uu.se/edu/course/homepage/sml/lectures/lecture3_handout.pdf

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   • Machine Learning in R - confusion matrix of an ensemble

     Top Answer

     Answered Jan 17, 2019 · 0 votes

     You are not training any ensemble; you are just training a list of several models, without combining them in any way, which is definitely not an ensemble.

     Given that, the error you get is not unexpected, since confusionMatrix expects a single prediction (which would be the case if you had an ensemble indeed), and not multiple ones.

     Keeping your list for simplicity to only your first 4 models, and changing slightly your fits_predicts definition, so that it gives a dataframe, i.e.:

     models <- c("glm", "lda", "naive_bayes", "svmLinear")-fits_predicts <- as.data.frame( sapply(fits, function(fits){ predict(fits,mnist_27$test)}))-# rest of your code as-is-

     here is how you can get the confusion matrices for each one of your models:

     cm <- lapply(fits_predicts, function(fits_predicts){confusionMatrix(fits_predicts,reference=(mnist_27$test$y))})-

     which gives

     > cm$glmConfusion Matrix and Statistics- ReferencePrediction 2 7 2 82 26 7 24 68- Accuracy : 0.75 95% CI : (0.684, 0.8084) No Information Rate : 0.53 P-Value [Acc > NIR] : 1.266e-10 - Kappa : 0.4976 Mcnemar's Test P-Value : 0.8875 - Sensitivity : 0.7736 Specificity : 0.7234 Pos Pred Value : 0.7593 Neg Pred Value : 0.7391 Prevalence : 0.5300 Detection Rate : 0.4100 Detection Prevalence : 0.5400 Balanced Accuracy : 0.7485 - 'Positive' Class : 2 --$ldaConfusion Matrix and Statistics- ReferencePrediction 2 7 2 82 26 7 24 68- Accuracy : 0.75 95% CI : (0.684, 0.8084) No Information Rate : 0.53 P-Value [Acc > NIR] : 1.266e-10 - Kappa : 0.4976 Mcnemar's Test P-Value : 0.8875 - Sensitivity : 0.7736 Specificity : 0.7234 Pos Pred Value : 0.7593 Neg Pred Value : 0.7391 Prevalence : 0.5300 Detection Rate : 0.4100 Detection Prevalence : 0.5400 Balanced Accuracy : 0.7485 - 'Positive' Class : 2 --$naive_bayesConfusion Matrix and Statistics- ReferencePrediction 2 7 2 88 23 7 18 71- Accuracy : 0.795 95% CI : (0.7323, 0.8487) No Information Rate : 0.53 P-Value [Acc > NIR] : 5.821e-15 - Kappa : 0.5873 Mcnemar's Test P-Value : 0.5322 - Sensitivity : 0.8302 Specificity : 0.7553 Pos Pred Value : 0.7928 Neg Pred Value : 0.7978 Prevalence : 0.5300 Detection Rate : 0.4400 Detection Prevalence : 0.5550 Balanced Accuracy : 0.7928 - 'Positive' Class : 2 --$svmLinearConfusion Matrix and Statistics- ReferencePrediction 2 7 2 81 24 7 25 70- Accuracy : 0.755 95% CI : (0.6894, 0.8129) No Information Rate : 0.53 P-Value [Acc > NIR] : 4.656e-11 - Kappa : 0.5085 Mcnemar's Test P-Value : 1 - Sensitivity : 0.7642 Specificity : 0.7447 Pos Pred Value : 0.7714 Neg Pred Value : 0.7368 Prevalence : 0.5300 Detection Rate : 0.4050 Detection Prevalence : 0.5250 Balanced Accuracy : 0.7544 - 'Positive' Class : 2 -

     And you can also access the individual confusion matrices per model, e.g. for lda:

     > cm['lda']$ldaConfusion Matrix and Statistics- ReferencePrediction 2 7 2 82 26 7 24 68- Accuracy : 0.75 95% CI : (0.684, 0.8084) No Information Rate : 0.53 P-Value [Acc > NIR] : 1.266e-10 - Kappa : 0.4976 Mcnemar's Test P-Value : 0.8875 - Sensitivity : 0.7736 Specificity : 0.7234 Pos Pred Value : 0.7593 Neg Pred Value : 0.7391 Prevalence : 0.5300 Detection Rate : 0.4100 Detection Prevalence : 0.5400 Balanced Accuracy : 0.7485 - 'Positive' Class : 2 -

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2. www.geeksforgeeks.org › confusion-matrix-machineConfusion Matrix in Machine Learning - GeeksforGeeks

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   May 13, 2024 · Learn how to use a confusion matrix to measure the performance of a classification model on a set of test data. See examples, metrics, and types of errors for binary and multiclass problems.

3. www.datacamp.com › tutorial › what-is-a-confusionWhat is A Confusion Matrix in Machine Learning? The Model ...

   www.datacamp.com › tutorial › what-is-a-confusion

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   Learn what a confusion matrix is, how to calculate it, and why it is useful for evaluating machine learning models. Explore the terminology, benefits, and limitations of using a confusion matrix and its metrics, such as accuracy, precision, recall, and F1 score.

4. machinelearningmastery.com › confusionWhat is a Confusion Matrix in Machine Learning

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   A confusion matrix is a technique for summarizing the performance of a classification algorithm. Learn how to calculate and interpret a confusion matrix for 2-class and multi-class problems, and see examples in Python and R.

5. People also ask

   What is a confusion matrix?

   • The confusion matrix provides a comprehensive evaluation of a classification model’s performance, offering insights into true positives, true negatives, false positives, and false negatives, aiding nuanced analysis beyond basic accuracy. 3.What are some examples of confusion matrix applications?

   Confusion Matrix in Machine Learning - GeeksforGeeks

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   How to calculate a confusion matrix in Python?

   • The scikit-learn library for machine learning in Python can calculate a confusion matrix. Given an array or list of expected values and a list of predictions from your machine learning model, the confusion_matrix () function will calculate a confusion matrix and return the result as an array. You can then print this array and interpret the results.

   What is a Confusion Matrix in Machine Learning

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   How accurate is a confusion matrix model?

   • The model’s confusion matrix shows only one false negative and no false positives; the model correctly classifies every other data instance. Thus the model has an accuracy of 99%. Though ostensibly desirable, high accuracy is not in itself indicative of excellent model performance.

   What is a confusion matrix? | IBM

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   What metrics are calculated from the confusion matrix?

   • From the confusion matrix, we can calculate many metrics like recall, precision,f1 score which is used to evaluate the performance of classification models. In this blog, we will learn about the confusion matrix and the metrics calculated from the confusion matrix. What we need Confusion Matrix? What is the Confusion Matrix? 2. Recall

   Confusion Matrix — Clearly Explained - Towards Data Science

   towardsdatascience.com/confusion-matrix-clearly-explained-fee63614dc7

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6. en.wikipedia.org › wiki › Confusion_matrixConfusion matrix - Wikipedia

   en.wikipedia.org › wiki › Confusion_matrix

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   Table of confusion. In predictive analytics, a table of confusion (sometimes also called a confusion matrix) is a table with two rows and two columns that reports the number of true positives, false negatives, false positives, and true negatives. This allows more detailed analysis than simply observing the proportion of correct classifications ...

7. www.ibm.com › topics › confusion-matrixWhat is a confusion matrix? | IBM

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   Learn what a confusion matrix is and how to use it to assess classification model performance in machine learning. See examples of binary and multiclass confusion matrices and how to calculate precision, recall, F1 score and other metrics.

8. Videos

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     Machine Learning Fundamentals: The Confusion Matrix

     Oct 29, 2018

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     Confusion Matrix in Machine Learning | Binary and Multiclass Classification Examples | Edureka

     Mar 8, 2022

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     11:05

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     Confusion Matrix In Machine Learning | Machine Learning Tutorial

     Dec 3, 2021

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9. towardsdatascience.com › understanding-confusionUnderstanding Confusion Matrix | by Sarang Narkhede | Towards ...

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   May 9, 2018 · Learn what confusion matrix is and why it is useful for measuring the performance of machine learning classification models. See how to calculate confusion matrix, recall, precision, accuracy, and F-score with examples and formulas.

10. towardsdatascience.com › confusion-matrix-clearlyConfusion Matrix — Clearly Explained - Towards Data Science

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    Dec 23, 2020 · Photo by Olya Kobruseva from Pexels Confusion Matrix. In machine learning, the confusion matrix helps to summarize the performance of classification models. From the confusion matrix, we can calculate many metrics like recall, precision,f1 score which is used to evaluate the performance of classification models.