confusion-matrix

来源： https://stackoverflow.com/questions/47708865/confusion-matrix-for-clustering-in-scikit-learn

来源： https://stackoverflow.com/questions/48872738/understanding-multi-label-classifier-using-confusion-matrix

来源： https://stackoverflow.com/questions/48872738/understanding-multi-label-classifier-using-confusion-matrix

问题 I want to plot a confusion matrix, but, I don't want to just use a heatmap, because I think they give poor numerical resolution. Instead, I want to also plot the frequency in the middle of the square. For instance, I like the output of this: library(mlearning); data("Glass", package = "mlbench") Glass$Type <- as.factor(paste("Glass", Glass$Type)) summary(glassLvq <- mlLvq(Type ~ ., data = Glass)); (glassConf <- confusion(predict(glassLvq, Glass, type = "class"), Glass$Type)) plot(glassConf) #

问题 I want to plot a confusion matrix, but, I don't want to just use a heatmap, because I think they give poor numerical resolution. Instead, I want to also plot the frequency in the middle of the square. For instance, I like the output of this: library(mlearning); data("Glass", package = "mlbench") Glass$Type <- as.factor(paste("Glass", Glass$Type)) summary(glassLvq <- mlLvq(Type ~ ., data = Glass)); (glassConf <- confusion(predict(glassLvq, Glass, type = "class"), Glass$Type)) plot(glassConf) #

问题 I like to use Plotly to visualize everything, I'm trying to visualize a confusion matrix by Plotly, this is my code: def plot_confusion_matrix(y_true, y_pred, class_names): confusion_matrix = metrics.confusion_matrix(y_true, y_pred) confusion_matrix = confusion_matrix.astype(int) layout = { "title": "Confusion Matrix", "xaxis": {"title": "Predicted value"}, "yaxis": {"title": "Real value"} } fig = go.Figure(data=go.Heatmap(z=confusion_matrix, x=class_names, y=class_names, hoverongaps=False),

问题 I like to use Plotly to visualize everything, I'm trying to visualize a confusion matrix by Plotly, this is my code: def plot_confusion_matrix(y_true, y_pred, class_names): confusion_matrix = metrics.confusion_matrix(y_true, y_pred) confusion_matrix = confusion_matrix.astype(int) layout = { "title": "Confusion Matrix", "xaxis": {"title": "Predicted value"}, "yaxis": {"title": "Real value"} } fig = go.Figure(data=go.Heatmap(z=confusion_matrix, x=class_names, y=class_names, hoverongaps=False),

问题 Can anyone explain how to interpret coefficientMatrix , interceptVector , Confusion matrix of a multinomial logistic regression . According to Spark documentation: Multiclass classification is supported via multinomial logistic (softmax) regression. In multinomial logistic regression, the algorithm produces K sets of coefficients, or a matrix of dimension K×J where K is the number of outcome classes and J is the number of features. If the algorithm is fit with an intercept term then a length

问题 Can anyone explain how to interpret coefficientMatrix , interceptVector , Confusion matrix of a multinomial logistic regression . According to Spark documentation: Multiclass classification is supported via multinomial logistic (softmax) regression. In multinomial logistic regression, the algorithm produces K sets of coefficients, or a matrix of dimension K×J where K is the number of outcome classes and J is the number of features. If the algorithm is fit with an intercept term then a length

问题 Can anyone explain how to interpret coefficientMatrix , interceptVector , Confusion matrix of a multinomial logistic regression . According to Spark documentation: Multiclass classification is supported via multinomial logistic (softmax) regression. In multinomial logistic regression, the algorithm produces K sets of coefficients, or a matrix of dimension K×J where K is the number of outcome classes and J is the number of features. If the algorithm is fit with an intercept term then a length