multiclass-classification

问题 I have a data having 3 class labels(0,1,2). I tried to make ROC curve. and did it by using pos_label parameter. fpr, tpr, thresholds = metrics.roc_curve(Ytest, y_pred_prob, pos_label = 0) By changing pos_label to 0,1,2- I get 3 graphs, Now I am having issue in calculating AUC score. How can I average the 3 graphs and plot 1 graph from it and then calculate the Roc_AUC score. i am having error in by this metrics.roc_auc_score(Ytest, y_pred_prob) ValueError: multiclass format is not supported

问题 How do you find the F1-score for each class of a multiclass Catboost Classifier? I've already read through the documentation and the github repo where someone asks the same question. However, I am unable to figure out the codesmithing to achieve this. I understand that I must use the custom_metric parameter in CatBoostClassifier() but I don't know what arguments are acceptable for custom_metric when I want F1 score for each class of my multiclass dataset. Suppose you have a toy dataset (from

问题 How do you find the F1-score for each class of a multiclass Catboost Classifier? I've already read through the documentation and the github repo where someone asks the same question. However, I am unable to figure out the codesmithing to achieve this. I understand that I must use the custom_metric parameter in CatBoostClassifier() but I don't know what arguments are acceptable for custom_metric when I want F1 score for each class of my multiclass dataset. Suppose you have a toy dataset (from

问题 How do you find the F1-score for each class of a multiclass Catboost Classifier? I've already read through the documentation and the github repo where someone asks the same question. However, I am unable to figure out the codesmithing to achieve this. I understand that I must use the custom_metric parameter in CatBoostClassifier() but I don't know what arguments are acceptable for custom_metric when I want F1 score for each class of my multiclass dataset. Suppose you have a toy dataset (from

问题 How do you find the F1-score for each class of a multiclass Catboost Classifier? I've already read through the documentation and the github repo where someone asks the same question. However, I am unable to figure out the codesmithing to achieve this. I understand that I must use the custom_metric parameter in CatBoostClassifier() but I don't know what arguments are acceptable for custom_metric when I want F1 score for each class of my multiclass dataset. Suppose you have a toy dataset (from

问题 Trying to understand how SVM-OVR (One-Vs-Rest) works, I was testing the following code: import matplotlib.pyplot as plt import numpy as np from sklearn.svm import SVC x = np.array([[1,1.1],[1,2],[2,1]]) y = np.array([0,100,250]) classifier = SVC(kernel='linear', decision_function_shape='ovr') classifier.fit(x,y) print(classifier.predict([[1,2]])) print(classifier.decision_function([[1,2]])) The outputs are: [100] [[ 1.05322128 2.1947332 -0.20488118]] It means that the sample [1,2] is

问题 Trying to understand how SVM-OVR (One-Vs-Rest) works, I was testing the following code: import matplotlib.pyplot as plt import numpy as np from sklearn.svm import SVC x = np.array([[1,1.1],[1,2],[2,1]]) y = np.array([0,100,250]) classifier = SVC(kernel='linear', decision_function_shape='ovr') classifier.fit(x,y) print(classifier.predict([[1,2]])) print(classifier.decision_function([[1,2]])) The outputs are: [100] [[ 1.05322128 2.1947332 -0.20488118]] It means that the sample [1,2] is

问题 In brief, I am trying to come up with a ML (and later DL model) for predicting control input variable of my computer simulation model, based on all other model input variables - let's call them environmental variables. Whether the simulation gives a convergent result or not depends on the value of the control variable. The database for the problem has been generated in a long, iterative simulation run with different scenarios. It consists of all environmental inputs, the control input value,

问题 In brief, I am trying to come up with a ML (and later DL model) for predicting control input variable of my computer simulation model, based on all other model input variables - let's call them environmental variables. Whether the simulation gives a convergent result or not depends on the value of the control variable. The database for the problem has been generated in a long, iterative simulation run with different scenarios. It consists of all environmental inputs, the control input value,

问题 I am following the documentation for plotting ROC curves for multiple classes at this link: http://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html I am confused about this line in particular: y_score = classifier.fit(X_train, y_train).decision_function(X_test) I've seen that in other examples, y_score holds probabilities, and they are all positive values, as we would expect. However, the y_score (each column for classes A-C) in this example has mostly negative values.