random-forest

问题 I'm looking to visualize a regression tree built using any of the ensemble methods in scikit learn (gradientboosting regressor, random forest regressor,bagging regressor). I've looked at this question which comes close, and this question which deals with classifier trees. But these questions require the 'tree' method, which is not available to the regression models in SKLearn. but it didn't seem to yield a result. I'm running into issues because there is no .tree method for the regression

问题 I am training a RandomForestClassifier (sklearn) to predict credit card fraud. When I then test the model and check the rocauc score i get different values when I use roc_auc_score and plot_roc_curve . roc_auc_score gives me around 0.89 and the plot_curve calculates AUC to 0.96 why is that? The labels are all 0 and 1 as well as the predictions are 0 or 1. CodE: clf = RandomForestClassifier(random_state =42) clf.fit(X_train, y_train[target].values) pred_test = clf.predict(X_test) print(roc_auc

问题 I am training a RandomForestClassifier (sklearn) to predict credit card fraud. When I then test the model and check the rocauc score i get different values when I use roc_auc_score and plot_roc_curve . roc_auc_score gives me around 0.89 and the plot_curve calculates AUC to 0.96 why is that? The labels are all 0 and 1 as well as the predictions are 0 or 1. CodE: clf = RandomForestClassifier(random_state =42) clf.fit(X_train, y_train[target].values) pred_test = clf.predict(X_test) print(roc_auc

问题 from sklearn.ensemble import RandomForestRegressor model=RandomForestRegressor() model.fit(X_train,y_train) model.score(X_test,y_test) feature_list = list(X.columns) r = export_text(model, feature_names=feature_list, decimals=0, show_weights=True) print(r) AttributeError: 'RandomForestRegressor' object has no attribute 'tree_' Any idea what I'm missing here? I am trying to get tree text data out of a random forest regressor 回答1: RandomForestRegressor is trained by fitting multiple trees,

问题 I am running into this situation where I have no clue what's going with the PySpark Random Forest classifier. I want the model to be reproducible given the same training data. To do so, I added the seed parameter to an integer value as recommended on this page. https://spark.apache.org/docs/2.4.1/api/java/org/apache/spark/mllib/tree/RandomForest.html. This seed parameter is the random seed for bootstrapping and choosing feature subsets. Now, I verified the model and they are absolutely

问题 I am running into this situation where I have no clue what's going with the PySpark Random Forest classifier. I want the model to be reproducible given the same training data. To do so, I added the seed parameter to an integer value as recommended on this page. https://spark.apache.org/docs/2.4.1/api/java/org/apache/spark/mllib/tree/RandomForest.html. This seed parameter is the random seed for bootstrapping and choosing feature subsets. Now, I verified the model and they are absolutely

问题 I am trying to build a model in R with random forest classification. (By editing the code by Ned Horning) I first used randomForest package but then found ranger , which promises faster calculations. At first, I used the code below to get predicted probabilities for each class at the end of the model with randomForest as: predProbs <- as.data.frame(predict(randfor, imageBlock, type='prob')) The type of probability here is as follows: We have 500 trees in the model and 250 of them says the

问题 Actually there is a lot of question about persistence,but i have tried a lot using pickle or joblib.dumps . but when i use it to save my random forest i got this: ValueError: ("Buffer dtype mismatch, expected 'SIZE_t' but got 'long'", <type 'sklearn.tree._tree.ClassificationCriterion'>, (1, array([10]))) Can any one tell me why? some code for review forest = RandomForestClassifier() forest.fit(data[:n_samples], target[:n_samples ]) import cPickle with open('rf.pkl', 'wb') as f: cPickle.dump

问题 I am working in python pandas (in a Jupyter notebook), where I created a Random Forest model for the Titanic data set. https://www.kaggle.com/c/titanic/data I read in the test and train data, then I clean it and I add new columns (the same columns to both). After fitting and re-fitting the model and trying boosts etc; I decide on one model: X2 = train_data[['Pclass','Sex','Age','richness']] rfc_model_3 = RandomForestClassifier(n_estimators=200) %time cross_val_score(rfc_model_3, X2, Y_target)

问题 I am trying to retain an ID on the row when predicting using a Random Forest model to merge back on to the original dataframe. I am using step_naomit in the recipe that removes the rows with missing data when I bake the training data, but also removes the records with missing data on the testing data. Unfortunately, I don't have an ID to easily know which records were removed so I can accurately merge back on the predictions. I have tried to add an ID column to the original data, but bake