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Python 2022-03-12 07:00:02
how to find the labels of the confusion matrix in python
""" In order to find the labels just use the Counter function to count the records from y_test and then check row-wise sum of the confusion matrix. Then apply the labels to the corresponding rows using the inbuilt seaborn plot as shown b... Add solution -
Python 2022-02-25 11:40:03
compute confusion matrix using python
import numpy as np currentDataClass = [1, 3, 3, 2, 5, 5, 3, 2, 1, 4, 3, 2, 1, 1, 2] predictedClass = [1, 2, 3, 4, 2, 3, 3, 2, 1, 2, 3, 1, 5, 1, 1] def comp_confmat(actual, predicted): classes = np.unique(actual) # extract the different classes ... Add solution -
Python 2022-02-21 06:20:05
best algorithm for classification
for clf in classifiers: clf.fit(X_train, y_train) y_pred= clf.predict(X_test) acc = accuracy_score(y_test, y_pred) print("Accuracy of %s is %s"%(clf, acc)) cm = confusion_matrix(y_test, y_pred) print("Confusion Matrix of %... Add solution -
Python 2021-11-21 15:43:15
confusion matrix with labels sklearn
By definition, entry i,j in a confusion matrix is the number of observations actually in group i, but predicted to be in group j. Scikit-Learn provides a confusion_matrix function: from sklearn.metrics import confusion_matrix y_actu = [2, 0, 2, 2, 0, 1... Add solution -
Python 2021-11-19 19:27:14
accuracy for each class
from sklearn.metrics import confusion_matrix import numpy as np y_true = [0, 1, 2, 2, 2] y_pred = [0, 0, 2, 2, 1] target_names = ['class 0', 'class 1', 'class 2'] #Get the confusion matrix cm = confusion_matrix(y_true, y_pred) #array([[1, 0, 0], # [1,... Add solution -
Python 2021-10-18 15:31:09
confusion matrix python
By definition, entry i,j in a confusion matrix is the number of observations actually in group i, but predicted to be in group j. Scikit-Learn provides a confusion_matrix function: from sklearn.metrics import confusion_matrix y_actu = [2, 0, 2, 2, 0, 1... Add solution -
Python 2021-09-25 19:23:03
precision and recall from confusion matrix python
from sklearn.metrics import confusion_matrix, plot_confusion_matrix clf = # define your classifier (Decision Tree, Random Forest etc.) clf.fit(X, y) # fit your classifier # make predictions with your classifier y_pred = clf.predict(X) # get tr... Add solution -
Python 2021-09-21 18:04:04
True Positive, True Negative, False Positive, False Negative in scikit learn
#According to scikit-learn documentation, #http://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html#sklearn.metrics.confusion_matrix #By definition a confusion matrix C is such that C[i, j] is equal to the number of observa... Add solution
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