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...
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...
random_state = 0) # Instantiate learning model (k = 3) classifier = KNeighborsClassifier(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...
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 ...
Python Confusion Matrix在Python 中绘制混淆矩阵 Python Boolean在Python 中将布尔值转换为整数 在Python 中获取布尔值的否定值 在Python 中将字符串转换为布尔值Python Binary在Python 中将二进制转换为十六进制 Python 中的二进制数表示 在Python 中将字符串转换为二进制 在Python 中转换二进制为 IntPython Hex在...
Typically, the assignment will happen in the local scope, but if the target name is already declared global or nonlocal, that declaration is honored. The precedence of the walrus operator can cause some confusion. It binds less tightly than all other operators except the comma, so you might ...
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。
import pandas as pd import numpy as np import math from sklearn.model_selection import train_test_split,cross_val_score # 数据分区库 import xgboost as xgb from sklearn.metrics import accuracy_score, auc, confusion_matrix, f1_score, \ precision_score, recall_score, roc_curve, roc_auc_score...
argmax(test_labels, axis=1) cm = confusion_matrix(test_true_labels, test_predicted_labels) cmd = ConfusionMatrixDisplay(confusion_matrix=cm) cmd.plot(include_values=True, cmap='viridis', ax=None, xticks_rotation='horizontal') plt.show() Powered By Classes 0, 1, 6, 7, 8, 9, ...
There's more to the confusion by the way,2.>>> class A: pass >>> isinstance(A, A) False >>> isinstance(type, type) True >>> isinstance(object, object) True3.>>> issubclass(int, object) True >>> issubclass(type, object) True >>> issubclass(object, type) False...