frominterpret.glassboximportExplainableBoostingClassifierebm=ExplainableBoostingClassifier()ebm.fit(X_train,y_train)# or substitute with LogisticRegression, DecisionTreeClassifier, RuleListClassifier, ...# EBM supports pandas dataframes, numpy arrays, and handles "string" data natively. ...
Derive an explanation for why the predicted rate of having an income over $50K is higher for men compared to women in the UCI MLAdult dataset. Load the data and prepare it for ML. importpandasaspdfromsklearn.ensembleimportRandomForestClassifierfromsklearn.model_selectionimporttrain_test_splitdf...
Python 3.8, GeoPandas 0.8 and Matplotlib 3.7 have been used to produce the images. Full size image The first wave Considering the order of decreasing relevance from top to bottom of the graph, it can be observed that the economic variables such as compensation of employees, GDP and number ...
Population: A population, in Biology, is a group of organisms that all belong to one species (can interbreed and produce reproductively viable offspring) that occupy the same geographic region at the same time. Answer and Explanation:1 Explain two ways to restore the Florida Panther populatio...
fromsklearn.datasetsimportload_irisfromsklearn.model_selectionimporttrain_test_splitfromsklearn.svmimportSVCimportpandasaspdfromrulexai.explainerimportExplainer# load iris datasetdata=load_iris()df=pd.DataFrame(data['data'],columns=data['feature_names'])df['class']=data['target']# train a SVM cl...
It is implemented in the statsmodels library in Python. In my particular case, I used to first filter highly correlation using a mutual information criterion (from a set of highly correlated features, I choose the one with the highest mutual info with respect of the target), then use VIF ...
pandas==1.5.3 prettytable==3.8.0 scikit_learn==1.2.2 torch==2.0.1 tqdm==4.65.0 Necessary files ## data ├── Dataloader.py ├── Encoders ├── TabularEncoder.py ## T-COL ├── ExtractProto.py ├── GLT.py ├── CreateCFsWithGLT.py ├── tree_path.json ├── vali_mo...
frominterpret.glassboximportExplainableBoostingClassifierebm=ExplainableBoostingClassifier()ebm.fit(X_train,y_train)# or substitute with LogisticRegression, DecisionTreeClassifier, RuleListClassifier, ...# EBM supports pandas dataframes, numpy arrays, and handles "string" data natively. ...
# EBM supports pandas dataframes, numpy arrays, and handles "string" data natively. Understand the model from interpret import show ebm_global = ebm.explain_global() show(ebm_global) Understand individual predictions ebm_local = ebm.explain_local(X_test, y_test) show(ebm_local) And if you...