() X_train_scaled = scaler.fit_transform(X_train) X_test_scaled = scaler.transform(X_test) # Train classifiers classifiers = { 'Logistic Regression': LogisticRegression(max_iter=1000), 'XGBoost': XGBClassifier(), 'SVC': SVC(probability=True), 'GaussianNB': GaussianNB(), 'Random Forest':...
complex类型允许您创建复数,包括虚数部分。 复数的一般形式是a + bj,其中a表示实数部分,b表示虚数部分,j表示虚数单位 # 创建虚数 z1 = 3 + 2j z2 = 1 - 4j # 打印虚数 print(z1) print(z2) # 获取虚数的实部和虚部 real_part = z1.real imaginary_part = z1.imag print(f"实部: {real_part...
Before diving into a course, you’ll want to research to ensure it’s a good fit for you. Key considerations include how long it takes to complete, whether there are any prerequisites and whether you’ll get a certificate of completion at the end. We’ve focused on these four criteria t...
In[22]:from scipyimportlinalg In[23]:arr=np.array([[1,2],...:[3,4]])In[24]:linalg.det(arr)Out[24]:-2.0In[25]:linalg.det(np.ones((3,4)))---ValueErrorTraceback(most recent call last)<ipython-input-25-375ad1d49940>in<module>()--->1linalg.det(np.ones((3,4)))/usr/l...
For more complex tasks, you may need to provide your own objects using Python classes or C language interfaces. But as you’ll see in later parts of this book, objects implemented manually are often built on top of built-in types such as lists and dictionaries. For instance, a stack ...
In the optimization example, you first found the minimum value in a mathematically clear function with only one variable. Then, you solved the more complex problem of maximizing your profit from selling stocks. Using minimize(), you found the optimal number of stocks to sell to a group of bu...
This article uses built-in Jupyter Notebooks to demonstrate function calls to revoscalepy. If you are new to this tool, the following screenshot illustrates how the pieces fit together and why it all "just works". The parent folder %ProgramFiles%\Microsoft\PyForMLS contains Anaconda plus the...
Common Mistake #1: Misusing expressions as defaults for function arguments Python allows you to specify that a function argument is optional by providing a default value for it. While this is a great feature of the language, it can lead to some confusion when the default value is mutable. Fo...
Simple is better than complex. Complex is better than complicated. Flat is better than nested. Sparse is better than dense. Readability counts. Special cases aren't special enough to break the rules. Although practicality beats purity.Errors should never pass silently. Unless explicitly silenced. ...
clf_linear=svm.SVC(kernel='linear').fit(x,y)clf_rbf=svm.SVC(kernel='rbf').fit(x,y)clf_sigmoid=svm.SVC(kernel='sigmoid').fit(x,y)#test,训练数据处理 y_test=test_data[['tag']]x_test=test_data.iloc[:,1:len(test_data.columns)]#模型效果对比 ...