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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 2021-10-14 19:41:07
standard import packages
# Import Packages # Array and Dataframe processing: import pandas as pd import numpy as np # Visualization: import seaborn as sns sns.set_style("darkgrid") import matplotlib.pyplot as plt %matplotlib inline import plotly.graph_objects as go im... 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 2021-09-25 18:15:07
write a Program in Python/R to Demonstrate naive bayes classification
>>> from sklearn.naive_bayes import GaussianNB >>> from sklearn.naive_bayes import MultinomialNB >>> from sklearn import datasets >>> from sklearn.metrics import confusion_matrix >>> from sklearn.model_selectio... Add solution -
Python 2021-09-17 21:02:06
from sklearn.metrics import confusion_matrix pred = model.predict(X_test) pred = np.argmax(pred,axis = 1) y_true = np.argmax(y_test,axis = 1)
from sklearn.metrics import confusion_matrix pred = model.predict(X_test) pred = np.argmax(pred,axis = 1) y_true = np.argmax(y_test,axis = 1) Add solution -
Python 2021-09-14 19:34:01
torch import
import torch import torch.nn as nn import torch.nn.functional as F from torch.utils.data import DataLoader from torchvision import datasets, transforms, models from torchvision.utils import make_grid import numpy as np import pandas as pd from sklearn.me... Add solution
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