学习机用皮带
wolvr的Python项目详细描述
高级机器学习实用程序库模块
安装
pip install wolvr
例1:naivebayes
^{pr2}$所选输出：
This demo uses a public dataset of SMS spam, which has a total of 5574 messages = 747 spam and 4827 ham (legitimate). The goal is to use 'term frequency in message' to predict whether the message is ham (class=0) or spam (class=1). Using a grid search and a multinomial naive bayes classifier, the best hyperparameters were found as following: Step1: Tokenizing text: CountVectorizer(analyzer = 'word', ngram_range = (1, 1)); Step2: Transforming from occurrences to frequency: TfidfTransformer(use_idf = True). The top 2 terms with highest probability of a message being a spam (the classification is either spam or ham): "claim": 80.73% "prize": 80.06% Application example: - Message: "URGENT! We are trying to contact U. Todays draw shows that you have won a 2000 prize GUARANTEED. Call 090 5809 4507 from a landline. Claim 3030. Valid 12hrs only." - Probability of class=1 (spam): 98.32% - Classification: spam
例2:k-最近邻
fromwolvrimportkNNkNN.demo("Social_Network_Ads")
所选输出：
This demo uses a public dataset of Social Network Ads, which is used to determine what audience a car company should target in its ads in order to sell a SUV on a social network website. Using a grid search and a kNN classifier, the best hyperparameters were found as following: Step1: scaler: StandardScaler(with_mean=True, with_std=True); Step2: classifier: kNN_classifier(n_neighbors=8, weights='uniform', p=1.189207115002721, metric='minkowski').
例3：决策边界比较
fromwolvrimportkNNkNN.demo("Social_Network_Ads")fromwolvrimportnaive_bayesasnbnb.demo("Social_Network_Ads")fromwolvrimportSVMSVM.demo("Social_Network_Ads")
模块：模型评估
| function | description |
|---|---|
| plot_confusion_matrix() | plots the confusion matrix, along with key statistics, and returns accuracy |
| plot_ROC_curve() | plots the ROC (Receiver Operating Characteristic) curve, along with statistics |
| plot_PR_curve() | plots the precision-recall curve, along with statistics |
| plot_ROC_and_PR_curves() | plots both the ROC and the precision-recall curves, along with statistics |
| demo() | provides a demo of the major functions in this module |
模块：Naivey_bayes
| function | description |
|---|---|
| naive_bayes_Bernoulli() | when X are independent binary variables (e.g., whether a word occurs in a document or not) |
| naive_bayes_multinomial() | when X are independent discrete variables with 3+ levels (e.g., term frequency in the document) |
| naive_bayes_Gaussian() | when X are continuous variables |
| demo() | provides a demo of selected functions in this module |
模块：kNN
| function | description |
|---|---|
| demo() | provides a demo of selected functions in this module |
模块：神经网络
| function | description |
|---|---|
| rnn() | Recurrent neural network |
| demo() | provides a demo of selected functions in this module |
模块：决策树
| function | description |
|---|---|
| boost() | Boosting |
| demo() | provides a demo of selected functions in this module |
- 项目
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