Juliana Guamá JulianaGuama

## roc.py
#https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html

from sklearn.metrics import roc_curve

y_true = np.array([1, 1, 2, 2])
y_pred = np.array([0.1, 0.4, 0.35, 0.8])

#pos_label é onde indica-se o valor da classe de interesse
fpr, tpr, thresholds = metrics.roc_curve(y, scores, pos_label=2)

## wiscosinCancer.py
from sklearn.datasets import load_breast_cancer

data = load_breast_cancer()
list(data.target_names)
#>>> ['malignant', 'benign']

## ConfusionMatrix.py
From sklearn.metrics import confusion_matrix
print( confusion_matrix(y_predito, y_real) )

## Matriz Confusão.csv

          
            
             
             Previstos

            
              
               
              Positivo
              Negativo

            
              Real
              Positivo
               VP
               FN

            
              
              Negativo
              FP
               VN
	#https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html

	from sklearn.metrics import roc_curve

	y_true = np.array([1, 1, 2, 2])
	y_pred = np.array([0.1, 0.4, 0.35, 0.8])

	#pos_label é onde indica-se o valor da classe de interesse
	fpr, tpr, thresholds = metrics.roc_curve(y, scores, pos_label=2)
	from sklearn.datasets import load_breast_cancer

	data = load_breast_cancer()
	list(data.target_names)
	#>>> ['malignant', 'benign']
	From sklearn.metrics import confusion_matrix
	print( confusion_matrix(y_predito, y_real) )