-
Notifications
You must be signed in to change notification settings - Fork 0
/
NaiveBayes.py
41 lines (32 loc) · 1.16 KB
/
NaiveBayes.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_iris
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import confusion_matrix,accuracy_score
import pandas as pd
# iris = load_iris()
# X = iris.data
# y = iris.target
df = pd.read_csv("Iris.csv")
X = df.iloc[: ,1:5]
y = df.iloc[:, -1:]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=30)
classifier = GaussianNB()
classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)
print("\naccuracy score: ",accuracy_score(y_test,y_pred))
print("\nconfusion matrix:\n",confusion_matrix(y_test,y_pred))
# from sklearn import datasets
# from sklearn import metrics
# from sklearn.naive_bayes import GaussianNB
# # load the iris datasets
# dataset = datasets.load_iris()
# # fit a Naive Bayes model to the data
# model = GaussianNB()
# model.fit(dataset.data, dataset.target)
# print(model)
# # make predictions
# expected = dataset.target
# predicted = model.predict(dataset.data)
# # summarize the fit of the model
# print(metrics.classification_report(expected, predicted))
# print(metrics.confusion_matrix(expected, predicted))