confusion-matrix

问题 I have a confusion matrix created with sklearn.metrics.confusion_matrix . Now, I would like to plot it with sklearn.metrics.plot_confusion_matrix , but the first parameter is the trained classifier, as specified in the documentation. The problem is that I don't have a classifier; the results were obtained doing manual calculations. Is it still possible to plot the confusion matrix in one line via scikit-learn, or do I have to code it myself with matplotlib? Thanks in advance! 回答1: The fact

问题 I am classifying mnist data using following Keras code. From confusion_matrix command of sklearn.metrics i got confusion matrix and from TruePositive= sum(numpy.diag(cm1)) command i am able to get True Positive. But i am confuse how to get True Negative , False Positive, False Negative. I read solution from here but user comments confuse me. please help to code to get parameters. from sklearn.metrics import confusion_matrix import keras from keras.datasets import mnist from keras.models

问题 I've created a confusion matrix from the observations and its predictions in 3 classes. classes=c("Underweight", "Normal", "Overweight") When I compute the confusion matrix, it organizes the classes in the table alphabetical. Here is my code. # Confusion matrix Observations <- bmi_classification(cross.m$bmi) Predicted <- bmi_classification(cross.m$cvpred) conf <- table(Predicted, Observations) library(caret) f.conf <- confusionMatrix(conf) print(f.conf) This produces this output: Confusion

问题 I've created a confusion matrix from the observations and its predictions in 3 classes. classes=c("Underweight", "Normal", "Overweight") When I compute the confusion matrix, it organizes the classes in the table alphabetical. Here is my code. # Confusion matrix Observations <- bmi_classification(cross.m$bmi) Predicted <- bmi_classification(cross.m$cvpred) conf <- table(Predicted, Observations) library(caret) f.conf <- confusionMatrix(conf) print(f.conf) This produces this output: Confusion

问题 I have two confusion matrices with calculated values as true positive (tp), false positives (fp), true negatives(tn) and false negatives (fn), corresponding to two different methods. I want to represent them as I believe facet grid or facet wrap can do this, but I find difficult to start. Here is the data of two confusion matrices corresponding to method1 and method2 dframe<-structure(list(label = structure(c(4L, 2L, 1L, 3L, 4L, 2L, 1L, 3L), .Label = c("fn", "fp", "tn", "tp"), class = "factor

问题 Sorry for all the purrr related questions today, still trying to figure out how to make efficient use of it. So with some help from SO I managed to get random forest ranger model running based on input values coming from a data.frame. This is accomplished using purrr::pmap . However, I don't understand how the return values are generated from the called function. Consider this example: library(ranger) data(iris) Input_list <- list(iris1 = iris, iris2 = iris) # let's assume these are different

问题 I am trying to understand how to make a confusion matrix and ROC curve for my multilabel classification problem. I am building a neural network. Here are my classes: mlb = MultiLabelBinarizer() ohe = mlb.fit_transform(as_list) # loop over each of the possible class labels and show them for (i, label) in enumerate(mlb.classes_): print("{}. {}".format(i + 1, label)) [INFO] class labels: 1. class1 2. class2 3. class3 4. class4 5. class5 6. class6 My labels are transformed: ohe array([[0, 1, 0, 0

问题 How can I make my mat plot lib interactive? For example, when I hover the mouse over each cell of a confusion matrix, I'd like to display the instance of that prediction. confusion_mat_df = pd.DataFrame(confusion_mat,columns = pred_spectrum, index = actual_spectrum) plt.figure(figsize=(7,5)) # width,height sns.heatmap(confusion_mat_df, annot=True) 回答1: Here is an example to illustrate how to use mplcursors for an sklearn confusion matrix. Unfortunately, mplcursors doesn't work with seaborn

问题 How can I make my mat plot lib interactive? For example, when I hover the mouse over each cell of a confusion matrix, I'd like to display the instance of that prediction. confusion_mat_df = pd.DataFrame(confusion_mat,columns = pred_spectrum, index = actual_spectrum) plt.figure(figsize=(7,5)) # width,height sns.heatmap(confusion_mat_df, annot=True) 回答1: Here is an example to illustrate how to use mplcursors for an sklearn confusion matrix. Unfortunately, mplcursors doesn't work with seaborn

问题 I am working with a confusion matrix (Figure A) How can I make my ticks to start from 1 to 3 instead of 0 to 2? I tried adding a +1 in tick_marks . But it does not work (Figure B) Check my code: import itertools cm = confusion_matrix(y_test, y_pred) np.set_printoptions(precision=2) print('Confusion matrix, without normalization') print(cm) plt.figure() plot_confusion_matrix(cm) def plot_confusion_matrix(cm, title='Confusion matrix', cmap=plt.cm.Oranges): plt.imshow(cm, interpolation='nearest'