from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
iris = load_iris()
print(iris)
X = iris.data
y = iris.target
print(X.shape)
print(y.shape)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=84)
model = GaussianNB()
model.fit(X_train, y_train)
print(model.score(X_train, y_train))
print(model.score(X_test, y_test))
print(model.predict(X_test[0:1]))
print(model.predict(y_test[0]))

d2b408f24bd2f587df74aef003b1f97337bfdc4a
<mask token>

<mask token>
print(iris)
<mask token>
print(X.shape)
print(y.shape)
<mask token>
model.fit(X_train, y_train)
print(model.score(X_train, y_train))
print(model.score(X_test, y_test))
print(model.predict(X_test[0:1]))
print(model.predict(y_test[0]))

<mask token>
iris = load_iris()
print(iris)
X = iris.data
y = iris.target
print(X.shape)
print(y.shape)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=84)
model = GaussianNB()
model.fit(X_train, y_train)
print(model.score(X_train, y_train))
print(model.score(X_test, y_test))
print(model.predict(X_test[0:1]))
print(model.predict(y_test[0]))

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
iris = load_iris()
print(iris)
X = iris.data
y = iris.target
print(X.shape)
print(y.shape)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=84)
model = GaussianNB()
model.fit(X_train, y_train)
print(model.score(X_train, y_train))
print(model.score(X_test, y_test))
print(model.predict(X_test[0:1]))
print(model.predict(y_test[0]))
