random-forest

问题 I have an imbalanced dataset containing binary classification problem.I have built Random Forest Classifier and used k fold cross validation with 10 folds. kfold = model_selection.KFold(n_splits=10, random_state=42) model=RandomForestClassifier(n_estimators=50) I got the results of the 10 folds results = model_selection.cross_val_score(model,features,labels, cv=kfold) print results [ 0.60666667 0.60333333 0.52333333 0.73 0.75333333 0.72 0.7 0.73 0.83666667 0.88666667] I have calculated

问题 I have an imbalanced dataset containing binary classification problem.I have built Random Forest Classifier and used k fold cross validation with 10 folds. kfold = model_selection.KFold(n_splits=10, random_state=42) model=RandomForestClassifier(n_estimators=50) I got the results of the 10 folds results = model_selection.cross_val_score(model,features,labels, cv=kfold) print results [ 0.60666667 0.60333333 0.52333333 0.73 0.75333333 0.72 0.7 0.73 0.83666667 0.88666667] I have calculated

问题 I have an imbalanced dataset containing binary classification problem.I have built Random Forest Classifier and used k fold cross validation with 10 folds. kfold = model_selection.KFold(n_splits=10, random_state=42) model=RandomForestClassifier(n_estimators=50) I got the results of the 10 folds results = model_selection.cross_val_score(model,features,labels, cv=kfold) print results [ 0.60666667 0.60333333 0.52333333 0.73 0.75333333 0.72 0.7 0.73 0.83666667 0.88666667] I have calculated

问题 I am trying to run a Random Forest Classifier on an imbalanced dataset (~1:4). I am using the method from imblearn as follows: from imblearn.ensemble import BalancedRandomForestClassifier rf=BalancedRandomForestClassifier(n_estimators=1000,random_state=42,class_weight='balanced',sampling_strategy='not minority') rf.fit(train_features,train_labels) predictions=rf.predict(test_features) The split in training and test set is performed within a cross-validation approach using

问题 The same question has been asked. But since the OP didn't post the code, not much helpful information was given. I'm having basically the same problem, where for some reason shuffling data is making a big accuracy gain (from 45% to 94%!) to my random forest classifier. (In my case removing duplicates also affected the accuracy, but that may be a discussion for another day) Based on my understanding on how RF algorithm works, this really should not happen. My data are merged from several files

来源： https://stackoverflow.com/questions/64501526/error-building-partial-dependence-plots-for-rf-using-finalmodel-output-from-care

来源： https://stackoverflow.com/questions/64501526/error-building-partial-dependence-plots-for-rf-using-finalmodel-output-from-care

来源： https://stackoverflow.com/questions/57307095/change-threshold-value-for-random-forest-classifier

来源： https://stackoverflow.com/questions/57307095/change-threshold-value-for-random-forest-classifier

来源： https://stackoverflow.com/questions/57307095/change-threshold-value-for-random-forest-classifier