import numpy as np
from sklearn.metrics import accuracy_score

y_pred = [0, 2, 1, 3]
y_true = [0, 1, 2, 3]
score1 = accuracy_score(y_true, y_pred)    
print(score1)

0.5

score2 = accuracy_score(y_true, y_pred, normalize=False)
print(score2)

2

import pydotplus
from sklearn.datasets import load_iris
from sklearn import tree
import collections

# Data Collection
#heigh, length of hair, voice pitch
X = [ [180, 15,0],     
      [177, 42,0],
      [136, 35,1],
      [174, 65,0],
      [141, 28,1 …

from sklearn.naive_bayes import GaussianNB
import numpy as np

#assigning predictor and target variables
x= np.array([[-3,7],[1,5], [1,2], [-2,0], [2,3], [-4,0], [-1,1], [1,1], [-2,2], [2,7], [-4,1 …

from sklearn.naive_bayes import GaussianNB
import numpy as np

#assigning predictor and target variables
x= np.array([[-3,7],[1,5], [1,2], [-2,0], [2,3], [-4,0], [-1,1], [1,1], [-2,2], [2,7], [-4,1], [-2,7]])
y = np.array([3, 3, 3, 3, 4, 3 …

!python --version

Python 3.12.4

from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import accuracy_score

# Load dataset
data = load_breast_cancer()

type(data)

sklearn.utils._bunch.Bunch

# Organize our data
label_names = data['target_names']
labels = data['target']
feature_names = data['feature_names']
features = data …

from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import accuracy_score

# Load dataset
data = load_breast_cancer()

type(data)

sklearn.utils.Bunch

# Organize our data
label_names = data['target_names']
labels …

from sklearn import datasets
from sklearn.naive_bayes import GaussianNB

iris = datasets.load_iris()

gnb = GaussianNB()
y_pred = gnb.fit(iris.data, iris.target).predict(iris.data)

print("Number of mislabeled points out of a total %d points : %d" % (iris …

from __future__ import print_function

from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import classification_report
from sklearn.svm import SVC

# print(__doc__)

# Loading the Digits dataset
digits = datasets.load_digits …

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection

from sklearn.linear_model import LinearRegression
from sklearn.isotonic import IsotonicRegression
from sklearn.utils import check_random_state

n = 100
x = np.arange(n)
rs = check_random_state(0)
y …

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection

from sklearn.linear_model import LinearRegression
from sklearn.isotonic import IsotonicRegression
from sklearn.utils import check_random_state

n = 100
x = np.arange(n)
rs = check_random_state(0)
y = rs.randint(-50, 50, size=(n,)) + 50. * np.log(1 + np …