Gaussian Naive Bayes ile Iris Veriseti Sınıflandırması
import matplotlib.pyplot as plt
import numpy as np
from IPython.display import Image,HTML
from sklearn import datasets
iris = datasets.load_iris()
HTML("""<style>
.output_png {text-align: center;}
</style>""")
Image(filename="C:\\Users\\ceakn\\Desktop\\1-site-resimler\\iris.png")
print(iris.feature_names)
['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']
print(iris.target_names)
['setosa' 'versicolor' 'virginica']
iris.data
array([[ 5.1, 3.5, 1.4, 0.2], [ 4.9, 3. , 1.4, 0.2], [ 4.7, 3.2, 1.3, 0.2], [ 4.6, 3.1, 1.5, 0.2], [ 5. , 3.6, 1.4, 0.2], [ 5.4, 3.9, 1.7, 0.4], [ 4.6, 3.4, 1.4, 0.3], [ 5. , 3.4, 1.5, 0.2], [ 4.4, 2.9, 1.4, 0.2], [ 4.9, 3.1, 1.5, 0.1], [ 5.4, 3.7, 1.5, 0.2], [ 4.8, 3.4, 1.6, 0.2], [ 4.8, 3. , 1.4, 0.1], [ 4.3, 3. , 1.1, 0.1], [ 5.8, 4. , 1.2, 0.2], [ 5.7, 4.4, 1.5, 0.4], [ 5.4, 3.9, 1.3, 0.4], [ 5.1, 3.5, 1.4, 0.3], [ 5.7, 3.8, 1.7, 0.3], [ 5.1, 3.8, 1.5, 0.3], [ 5.4, 3.4, 1.7, 0.2], [ 5.1, 3.7, 1.5, 0.4], [ 4.6, 3.6, 1. , 0.2], [ 5.1, 3.3, 1.7, 0.5], [ 4.8, 3.4, 1.9, 0.2], [ 5. , 3. , 1.6, 0.2], [ 5. , 3.4, 1.6, 0.4], [ 5.2, 3.5, 1.5, 0.2], [ 5.2, 3.4, 1.4, 0.2], [ 4.7, 3.2, 1.6, 0.2], [ 4.8, 3.1, 1.6, 0.2], [ 5.4, 3.4, 1.5, 0.4], [ 5.2, 4.1, 1.5, 0.1], [ 5.5, 4.2, 1.4, 0.2], [ 4.9, 3.1, 1.5, 0.1], [ 5. , 3.2, 1.2, 0.2], [ 5.5, 3.5, 1.3, 0.2], [ 4.9, 3.1, 1.5, 0.1], [ 4.4, 3. , 1.3, 0.2], [ 5.1, 3.4, 1.5, 0.2], [ 5. , 3.5, 1.3, 0.3], [ 4.5, 2.3, 1.3, 0.3], [ 4.4, 3.2, 1.3, 0.2], [ 5. , 3.5, 1.6, 0.6], [ 5.1, 3.8, 1.9, 0.4], [ 4.8, 3. , 1.4, 0.3], [ 5.1, 3.8, 1.6, 0.2], [ 4.6, 3.2, 1.4, 0.2], [ 5.3, 3.7, 1.5, 0.2], [ 5. , 3.3, 1.4, 0.2], [ 7. , 3.2, 4.7, 1.4], [ 6.4, 3.2, 4.5, 1.5], [ 6.9, 3.1, 4.9, 1.5], [ 5.5, 2.3, 4. , 1.3], [ 6.5, 2.8, 4.6, 1.5], [ 5.7, 2.8, 4.5, 1.3], [ 6.3, 3.3, 4.7, 1.6], [ 4.9, 2.4, 3.3, 1. ], [ 6.6, 2.9, 4.6, 1.3], [ 5.2, 2.7, 3.9, 1.4], [ 5. , 2. , 3.5, 1. ], [ 5.9, 3. , 4.2, 1.5], [ 6. , 2.2, 4. , 1. ], [ 6.1, 2.9, 4.7, 1.4], [ 5.6, 2.9, 3.6, 1.3], [ 6.7, 3.1, 4.4, 1.4], [ 5.6, 3. , 4.5, 1.5], [ 5.8, 2.7, 4.1, 1. ], [ 6.2, 2.2, 4.5, 1.5], [ 5.6, 2.5, 3.9, 1.1], [ 5.9, 3.2, 4.8, 1.8], [ 6.1, 2.8, 4. , 1.3], [ 6.3, 2.5, 4.9, 1.5], [ 6.1, 2.8, 4.7, 1.2], [ 6.4, 2.9, 4.3, 1.3], [ 6.6, 3. , 4.4, 1.4], [ 6.8, 2.8, 4.8, 1.4], [ 6.7, 3. , 5. , 1.7], [ 6. , 2.9, 4.5, 1.5], [ 5.7, 2.6, 3.5, 1. ], [ 5.5, 2.4, 3.8, 1.1], [ 5.5, 2.4, 3.7, 1. ], [ 5.8, 2.7, 3.9, 1.2], [ 6. , 2.7, 5.1, 1.6], [ 5.4, 3. , 4.5, 1.5], [ 6. , 3.4, 4.5, 1.6], [ 6.7, 3.1, 4.7, 1.5], [ 6.3, 2.3, 4.4, 1.3], [ 5.6, 3. , 4.1, 1.3], [ 5.5, 2.5, 4. , 1.3], [ 5.5, 2.6, 4.4, 1.2], [ 6.1, 3. , 4.6, 1.4], [ 5.8, 2.6, 4. , 1.2], [ 5. , 2.3, 3.3, 1. ], [ 5.6, 2.7, 4.2, 1.3], [ 5.7, 3. , 4.2, 1.2], [ 5.7, 2.9, 4.2, 1.3], [ 6.2, 2.9, 4.3, 1.3], [ 5.1, 2.5, 3. , 1.1], [ 5.7, 2.8, 4.1, 1.3], [ 6.3, 3.3, 6. , 2.5], [ 5.8, 2.7, 5.1, 1.9], [ 7.1, 3. , 5.9, 2.1], [ 6.3, 2.9, 5.6, 1.8], [ 6.5, 3. , 5.8, 2.2], [ 7.6, 3. , 6.6, 2.1], [ 4.9, 2.5, 4.5, 1.7], [ 7.3, 2.9, 6.3, 1.8], [ 6.7, 2.5, 5.8, 1.8], [ 7.2, 3.6, 6.1, 2.5], [ 6.5, 3.2, 5.1, 2. ], [ 6.4, 2.7, 5.3, 1.9], [ 6.8, 3. , 5.5, 2.1], [ 5.7, 2.5, 5. , 2. ], [ 5.8, 2.8, 5.1, 2.4], [ 6.4, 3.2, 5.3, 2.3], [ 6.5, 3. , 5.5, 1.8], [ 7.7, 3.8, 6.7, 2.2], [ 7.7, 2.6, 6.9, 2.3], [ 6. , 2.2, 5. , 1.5], [ 6.9, 3.2, 5.7, 2.3], [ 5.6, 2.8, 4.9, 2. ], [ 7.7, 2.8, 6.7, 2. ], [ 6.3, 2.7, 4.9, 1.8], [ 6.7, 3.3, 5.7, 2.1], [ 7.2, 3.2, 6. , 1.8], [ 6.2, 2.8, 4.8, 1.8], [ 6.1, 3. , 4.9, 1.8], [ 6.4, 2.8, 5.6, 2.1], [ 7.2, 3. , 5.8, 1.6], [ 7.4, 2.8, 6.1, 1.9], [ 7.9, 3.8, 6.4, 2. ], [ 6.4, 2.8, 5.6, 2.2], [ 6.3, 2.8, 5.1, 1.5], [ 6.1, 2.6, 5.6, 1.4], [ 7.7, 3. , 6.1, 2.3], [ 6.3, 3.4, 5.6, 2.4], [ 6.4, 3.1, 5.5, 1.8], [ 6. , 3. , 4.8, 1.8], [ 6.9, 3.1, 5.4, 2.1], [ 6.7, 3.1, 5.6, 2.4], [ 6.9, 3.1, 5.1, 2.3], [ 5.8, 2.7, 5.1, 1.9], [ 6.8, 3.2, 5.9, 2.3], [ 6.7, 3.3, 5.7, 2.5], [ 6.7, 3. , 5.2, 2.3], [ 6.3, 2.5, 5. , 1.9], [ 6.5, 3. , 5.2, 2. ], [ 6.2, 3.4, 5.4, 2.3], [ 5.9, 3. , 5.1, 1.8]])
print(iris.target)
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2]
print(type(iris.data))
print(type(iris.target))
print(type(iris))
print(iris.data.shape)
<class 'numpy.ndarray'> <class 'numpy.ndarray'> <class 'sklearn.datasets.base.Bunch'> (150, 4)
np.isnan(iris.data).any()
np.isnan(iris.target).any()
False
X = iris.data
y = iris.target
Gerekli Kütüphanenin İçe Aktarılması
from sklearn.naive_bayes import GaussianNB
classifier = GaussianNB()
classifier.fit(X,y)
GaussianNB(priors=None)
Eğitim ve Test Kümelerine Ayırma (Train/Test split)
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=4)
print(X_train.shape ,X_test.shape)
(100, 4) (50, 4)
print(y_train.shape ,y_test.shape)
(100,) (50,)
y_pred = classifier.predict(X_test)
Hata Oranını Bulalım
from sklearn.metrics import accuracy_score
print(accuracy_score(y_test, y_pred))
0.98
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
cm
array([[23, 0, 0], [ 0, 12, 0], [ 0, 1, 14]])
1 hatalı sonucumuz varmış