import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import (
confusion_matrix, accuracy_score, precision_score,
recall_score, f1_score, classification_report, roc_curve)
from sklearn.datasets import load_iris
import matplotlib.pyplot as plt
import seaborn as snsiris = load_iris()features = iris.data
target = iris.target
train_features, test_features, train_target, test_target = train_test_split(features, target, test_size=1/3, random_state=21)
knn = KNeighborsClassifier()
knn.fit(train_features, train_target)
predictions = knn.predict(test_features)Accuracy score
print(accuracy_score(test_target, predictions))0.94
Confustion matrix
cm = confusion_matrix(test_target, predictions)
print(cm)[[18 0 0]
[ 0 16 1]
[ 0 2 13]]
Classification report
print(classification_report(test_target, predictions)) precision recall f1-score support
0 1.00 1.00 1.00 18
1 0.89 0.94 0.91 17
2 0.93 0.87 0.90 15
accuracy 0.94 50
macro avg 0.94 0.94 0.94 50
weighted avg 0.94 0.94 0.94 50
Prikaz matrice zbunjenosti pomoću heat mape
fig, ax = plt.subplots(1, 1, figsize=(4, 4))
ax = sns.heatmap(cm, annot=True, square=True,
xticklabels=iris.target_names,
yticklabels=iris.target_names)
ax.set_xlabel('Predikcija')
ax.set_ylabel('Stvarno')
Makro preciznost
# Postupak računa
n_labels = len(np.unique(iris.target_names))
macro_precision = (np.diag(cm)/cm.sum(axis=0)).sum()/n_labels
print(macro_precision)0.9391534391534391
# Using sklearn metrics
macro_precision_f = precision_score(test_target, predictions, average='macro')
print(macro_precision_f)0.9391534391534391