一、机器学习和深度学习简介 深度学习的主题最近非常受欢迎,在这个过程中,出现了几个术语,使区分它们变得相当复杂。人们可能会发现,由于主题之间大量的重叠,将每个领域整齐地分开是一项艰巨的任务。 本章通过讨论深度学习的历史背景以及该领域如何演变成今天的形式来介绍深度学习的主题。稍后,我们将通过简要介绍基础主题来...
fromsklearn.metricsimportconfusion_matrix# 导入confusion_matrix函数 # 读取CSV文件 file_name ='creditcard_2023.csv' data = pd.read_csv(file_name) cm_2 = confusion_matrix(y_test, y_pred_svm) cmn_2 = cm_2.astype('float') / cm_2.sum(axis=1)[:, np.newaxis] fig, ax = plt.subplots...
i in other_word_index.items(): if word_index_dict.get(word) is not None: embedding_vector = other_embedding[i] this_vocab_word_indx = word_index_dict.get(word) self.embedding_matrix[this_vocab_word_indx] = embedding_vector num_updated+=1 print('{} words ...
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(n_neighbors=3) # Fitting the model classifier.fit(X_train, y_train) # Predicting the Test set results y_pred = classifier.predict(X_test) cm = confusion_matrix(y_test, y_pred) accuracy = accuracy_score(y_test, y_pred)*100 print('Accuracy of our model is equal ' + str(round(...
To avoid confusion, remember that you’re trying to determine if the value is not part of a given collection of values.Note: The not value in collection construct works the same as the value not in collection one. However, the former construct is more difficult to read. Therefore, you ...
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay from scipy.stats import pearsonr, ttest_ind, levene from radiomics import featureextractor 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 1、特征提取 提取特征主要使用python中的featureextractor这个包完成。
model_confusionmetricsmodelxgb, X_train, y_train, 'train') model_confusion_metrics(model_xgb, X_test, y_test, 'test') model_core_metrics(model_xgb, X_train, y_train, 'train') model_core_metrics(model_xgb, X_test, y_test, 'test') 1. 2. 3. 4. 5. 6. confusion matrix ...
This can be achieved from the confusion matrix, which shows for each class the number of correct and wrong predictions. The implementation is given below. We start by making predictions on the test data, then compute the confusion matrix and show the final result. from sklearn.metrics import ...
def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues): import itertools plt.imshow(cm, interpolation='nearest', cmap=cmap) plt.title(title) plt.colorbar() tick_marks = np.arange(len(classes)) ...