iris_dt_multiclassevaluation.py

from sklearn import svm, datasets
from sklearn.model_selection import train_test_split
import numpy as np
from sklearn.metrics import precision_recall_curve
from sklearn.metrics import roc_curve, auc
# from sklearn.metrics import plot_precision_recall_curve
import matplotlib.pyplot as plt

iris = datasets.load_iris()
X = iris.data
y = iris.target

# Add noisy features
random_state = np.random.RandomState(0)
n_samples, n_features = X.shape
X = np.c_[X, random_state.randn(n_samples, 200 * n_features)]

from sklearn.model_selection import train_test_split
from sklearn.svm import SVC

X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
svc = SVC(kernel='rbf', C=1).fit(X_train, y_train)
y_pred = svc.predict(X_test)

#importing confusion matrix
from sklearn.metrics import confusion_matrix
confusion = confusion_matrix(y_test, y_pred)
print('Confusion Matrix\n')
print(confusion)

#importing accuracy_score, precision_score, recall_score, f1_score
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
print('\nAccuracy: {:.2f}\n'.format(accuracy_score(y_test, y_pred)))

print('Micro Precision: {:.2f}'.format(precision_score(y_test, y_pred, average='micro')))
print('Micro Recall: {:.2f}'.format(recall_score(y_test, y_pred, average='micro')))
print('Micro F1-score: {:.2f}\n'.format(f1_score(y_test, y_pred, average='micro')))

print('Macro Precision: {:.2f}'.format(precision_score(y_test, y_pred, average='macro')))
print('Macro Recall: {:.2f}'.format(recall_score(y_test, y_pred, average='macro')))
print('Macro F1-score: {:.2f}\n'.format(f1_score(y_test, y_pred, average='macro')))

print('Weighted Precision: {:.2f}'.format(precision_score(y_test, y_pred, average='weighted')))
print('Weighted Recall: {:.2f}'.format(recall_score(y_test, y_pred, average='weighted')))
print('Weighted F1-score: {:.2f}'.format(f1_score(y_test, y_pred, average='weighted')))

from sklearn.metrics import classification_report
print('\nClassification Report\n')
print(classification_report(y_test, y_pred, target_names=['Class 1', 'Class 2', 'Class 3']))