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gauss naive bayes
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.pipeline import make_pipeline
from sklearn.naive_bayes import GaussianNB
from sklearn.preprocessing import QuantileTransformer
from sklearn.metrics import roc_curve, auc'bmh')
plt.rcParams['figure.figsize'] = (10, 10)
title_config = {'fontsize': 20, 'y': 1.05}
train = pd.read_csv('D:\\Documents\\Desktop\\train.csv')
X_train = train.iloc[:, 2:].values.astype('float64')
y_train = train['target'].values
pipeline = make_pipeline(QuantileTransformer(output_distribution='normal'), GaussianNB()), y_train)
fpr, tpr, thr = roc_curve(y_train, pipeline.predict_proba(X_train)[:,1])
plt.plot(fpr, tpr)
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver Operating Characteristic Plot', **title_config)
auc(fpr, tpr)
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