标签：sel report ima kmeans 规则 mode 语言 cal 学习
分析训练完成的机器学习模型的性能是任何机器学习工作流程中必不可少的步骤。 在PyCaret中分析模型性能就像编写plot_model一样简单。 该函数将受训的模型对象和图的类型作为plot_model函数中的字符串。
分类：
| Name | Plot |
| Area Under the Curve | ‘auc’ |
| Discrimination Threshold | ‘threshold’ |
| Precision Recall Curve | ‘pr’ |
| Confusion Matrix | ‘confusion_matrix’ |
| Class Prediction Error | ‘error’ |
| Classification Report | ‘class_report’ |
| Decision Boundary | ‘boundary’ |
| Recursive Feature Selection | ‘rfe’ |
| Learning Curve | ‘learning’ |
| Manifold Learning | ‘manifold’ |
| Calibration Curve | ‘calibration’ |
| Validation Curve | ‘vc’ |
| Dimension Learning | ‘dimension’ |
| Feature Importance | ‘feature’ |
| Model Hyperparameter | ‘parameter’ |
例子：
# Importing dataset from pycaret.datasets import get_data diabetes = get_data(‘diabetes‘) # Importing module and initializing setup from pycaret.classification import * clf1 = setup(data = diabetes, target = ‘Class variable‘) # creating a model lr = create_model(‘lr‘) # plotting a model plot_model(lr)
回归：
| Name | Plot |
| Residuals Plot | ‘residuals’ |
| Prediction Error Plot | ‘error’ |
| Cooks Distance Plot | ‘cooks’ |
| Recursive Feature Selection | ‘rfe’ |
| Learning Curve | ‘learning’ |
| Validation Curve | ‘vc’ |
| Manifold Learning | ‘manifold’ |
| Feature Importance | ‘feature’ |
| Model Hyperparameter | ‘parameter’ |
例子：
# Importing dataset from pycaret.datasets import get_data boston = get_data(‘boston‘) # Importing module and initializing setup from pycaret.regression import * reg1 = setup(data = boston, target = ‘medv‘) # creating a model lr = create_model(‘lr‘) # plotting a model plot_model(lr)
聚类：
| Name | Plot |
| Cluster PCA Plot (2d) | ‘cluster’ |
| Cluster TSnE (3d) | ‘tsne’ |
| Elbow Plot | ‘elbow’ |
| Silhouette Plot | ‘silhouette’ |
| Distance Plot | ‘distance’ |
| Distribution Plot | ‘distribution’ |
例子：
# Importing dataset from pycaret.datasets import get_data jewellery = get_data(‘jewellery‘) # Importing module and initializing setup from pycaret.clustering import * clu1 = setup(data = jewellery) # creating a model kmeans = create_model(‘kmeans‘) # plotting a model plot_model(kmeans)
异常检测：
| Name | Plot |
| t-SNE (3d) Dimension Plot | ‘tsne’ |
| UMAP Dimensionality Plot | ‘umap’ |
例子：
# Importing dataset from pycaret.datasets import get_data anomalies = get_data(‘anomaly‘) # Importing module and initializing setup from pycaret.anomaly import * ano1 = setup(data = anomalies) # creating a model iforest = create_model(‘iforest‘) # plotting a model plot_model(iforest)
自然语言处理：
| Name | Plot |
| Word Token Frequency | ‘frequency’ |
| Word Distribution Plot | ‘distribution’ |
| Bigram Frequency Plot | ‘bigram’ |
| Trigram Frequency Plot | ‘trigram’ |
| Sentiment Polarity Plot | ‘sentiment’ |
| Part of Speech Frequency | ‘pos’ |
| t-SNE (3d) Dimension Plot | ‘tsne’ |
| Topic Model (pyLDAvis) | ‘topic_model’ |
| Topic Infer Distribution | ‘topic_distribution’ |
| Word cloud | ‘wordcloud’ |
| UMAP Dimensionality Plot | ‘umap’ |
例子：
# Importing dataset from pycaret.datasets import get_data kiva = get_data(‘kiva‘) # Importing module and initializing setup from pycaret.nlp import * nlp1 = setup(data = kiva, target = ‘en‘) # creating a model lda = create_model(‘lda‘) # plotting a model plot_model(lda)
关联规则挖掘：
| Plot | Abbrev. String |
| Support, Confidence and Lift (2d) | ‘frequency’ |
| Support, Confidence and Lift (3d) | ‘distribution’ |
例子：
# Importing dataset from pycaret.datasets import get_data france = get_data(‘france‘) # Importing module and initializing setup from pycaret.arules import * arul1 = setup(data = france, transaction_id = ‘Invoice‘, item_id = ‘Description‘) # creating a model model = create_model(metric = ‘confidence‘) # plotting a model plot_model(model)
标签：sel report ima kmeans 规则 mode 语言 cal 学习
原文地址：https://www.cnblogs.com/xiximayou/p/13799181.html
