confusion-matrix

问题 I did a classification with svm using e1071 . The goal is to predict type through all other variables in dtm . dtm[140:145] %>% str() 'data.frame': 385 obs. of 6 variables: $ think : num 0 0 0 0 0 0 0 0 0 0 ... $ actually: num 0 0 0 0 0 0 0 0 0 0 ... $ comes : num 0 0 0 0 0 0 0 0 0 0 ... $ able : num 0 0 0 0 0 0 0 0 0 0 ... $ hours : num 0 0 0 0 0 0 0 0 0 0 ... $ type : Factor w/ 4 levels "-1","0","1","9": 4 3 3 3 4 1 4 4 4 3 ... To train/test the model, I used the 10-fold-cross-validation.

问题 I am using StratifiedKFold to checking the performance of my classifier. I have two classes and I trying to build Logistic Regression classier. Here is my code skf = StratifiedKFold(n_splits=10, shuffle=True, random_state=0) for train_index, test_index in skf.split(x, y): x_train, x_test = x[train_index], x[test_index] y_train, y_test = y[train_index], y[test_index] tfidf = TfidfVectorizer() x_train = tfidf.fit_transform(x_train) x_test = tfidf.transform(x_test) clf = LogisticRegression(class

问题 I am using StratifiedKFold to checking the performance of my classifier. I have two classes and I trying to build Logistic Regression classier. Here is my code skf = StratifiedKFold(n_splits=10, shuffle=True, random_state=0) for train_index, test_index in skf.split(x, y): x_train, x_test = x[train_index], x[test_index] y_train, y_test = y[train_index], y[test_index] tfidf = TfidfVectorizer() x_train = tfidf.fit_transform(x_train) x_test = tfidf.transform(x_test) clf = LogisticRegression(class

问题 Here is the config of my model : "model": { "loss": "categorical_crossentropy", "optimizer": "adam", "layers": [ { "type": "lstm", "neurons": 180, "input_timesteps": 15, "input_dim": 103, "return_seq": true, "activation": "relu" }, { "type": "dropout", "rate": 0.1 }, { "type": "lstm", "neurons": 100, "activation": "relu", "return_seq": false }, { "type": "dropout", "rate": 0.1 }, { "type": "dense", "neurons": 30, "activation": "relu" }, { "type": "dense", "neurons": 3, "activation": "softmax"

问题 I adapted a heatmap plot for a confusion matrix from this answer. However I would like to twist it. In the diagonal (from top left to bottom right) are the matches (correct classifications). My aim would be, to plot this diagonal in a yellow color palette. And mismatches (so all tiles except those in the diagonal) in a red color palette. In my plot.cm function I can get the diagonal with cm_d$diag <- cm_d$Prediction == cm_d$Reference # Get the Diagonal cm_d$ndiag <- cm_d$Prediction != cm_d

问题 Suppose I have a factor variable y with n levels, for which I have both predictions and real outcomes available. How can I construct the confusion matrix? set.seed(12345) y_actual = as.factor(sample(c('A','B', 'C', 'D', 'E'), 100, replace = TRUE)) set.seed(12346) y_predict = as.factor(sample(c('A','B', 'C', 'D', 'E'), 100, replace = TRUE)) This question is already answered for the case n = 2. See R: how to make a confusion matrix for a predictive model? What I tried This is how far I got ones

问题 Suppose I have a factor variable y with n levels, for which I have both predictions and real outcomes available. How can I construct the confusion matrix? set.seed(12345) y_actual = as.factor(sample(c('A','B', 'C', 'D', 'E'), 100, replace = TRUE)) set.seed(12346) y_predict = as.factor(sample(c('A','B', 'C', 'D', 'E'), 100, replace = TRUE)) This question is already answered for the case n = 2. See R: how to make a confusion matrix for a predictive model? What I tried This is how far I got ones

问题 Suppose I have a factor variable y with n levels, for which I have both predictions and real outcomes available. How can I construct the confusion matrix? set.seed(12345) y_actual = as.factor(sample(c('A','B', 'C', 'D', 'E'), 100, replace = TRUE)) set.seed(12346) y_predict = as.factor(sample(c('A','B', 'C', 'D', 'E'), 100, replace = TRUE)) This question is already answered for the case n = 2. See R: how to make a confusion matrix for a predictive model? What I tried This is how far I got ones

问题 Background In a confusion matrix, the diagonal represents the cases that the predicted label matches the correct label. So the diagonal is good, while all other cells are bad. To clarify what is good and what is bad in a CM for non-experts, I want to give the diagonal a different color than the rest. I want to achieve this with Python & Seaborn . Basically I'm trying to achieve what this question does in R (ggplot2 Heatmap 2 Different Color Schemes - Confusion Matrix: Matches in Different

问题 I calculated a confusion matrix for my classifier using the method confusion_matrix() from the sklearn package. The diagonal elements of the confusion matrix represent the number of points for which the predicted label is equal to the true label, while off-diagonal elements are those that are mislabeled by the classifier. I would like to normalize my confusion matrix so that it contains only numbers between 0 and 1. I would like to read the percentage of correctly classified samples from the