random-forest

问题 Im trying to create a Random Forest model with GridSearchCV but am getting an error pertaining to param_grid: "ValueError: Invalid parameter max_features for estimator Pipeline. Check the list of available parameters with `estimator.get_params().keys()" . I'm classifying documents so I am also pushing tf-idf vectorizer to the pipeline. Here is the code: from sklearn import metrics from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import classification_report, f1_score,

问题 Im trying to create a Random Forest model with GridSearchCV but am getting an error pertaining to param_grid: "ValueError: Invalid parameter max_features for estimator Pipeline. Check the list of available parameters with `estimator.get_params().keys()" . I'm classifying documents so I am also pushing tf-idf vectorizer to the pipeline. Here is the code: from sklearn import metrics from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import classification_report, f1_score,

问题 I am working on running RandomForest. I've imported point data representing used and unused sites and created a raster stack from raster GIS layers. I've created a SpatialPointDataFrame with all of my used and unused points with their underlying raster values attached. require(sp) require(rgdal) require(raster) #my raster stack xvariables <- stack(rlist) #rlist = a list of raster layers # Reading in the spatial used and unused points. ldata <- readOGR(dsn=paste(path, "DATA", sep="/"), layer

问题 I am working on running RandomForest. I've imported point data representing used and unused sites and created a raster stack from raster GIS layers. I've created a SpatialPointDataFrame with all of my used and unused points with their underlying raster values attached. require(sp) require(rgdal) require(raster) #my raster stack xvariables <- stack(rlist) #rlist = a list of raster layers # Reading in the spatial used and unused points. ldata <- readOGR(dsn=paste(path, "DATA", sep="/"), layer

问题 When training on even small applications (<50K rows <50 columns) using the mean absolute error criterion for sklearn's RandomForestRegress is nearly 10x slower than using mean squared error. To illustrate even on a small data set: import time from sklearn.ensemble import RandomForestRegressor from sklearn.datasets import load_boston X, y = load_boston(return_X_y=True) def fit_rf_criteria(criterion, X=X, y=y): reg = RandomForestRegressor(n_estimators=100, criterion=criterion, n_jobs=-1, random

问题 We have trained an Extra Tree model for some regression task. Our model consists of 3 extra trees, each having 200 trees of depth 30. On top of the 3 extra trees, we use a ridge regression. We train our model for several hours and pickle the trained model (the entire class object), for later use. However, the size of saved trained model is too big, about 140 GB! Is there a way to reduce the size of the saved model? are there any configuration in pickle that could be helpful, or any

问题 Other random forest tools have the "dial" that limits max depth of splits on a particular branch. h2o.randomForest has "max_depth", for example. What is the version of that for "ranger"? 回答1: I'm not familiar with the h2o.randomForest package, but my general understanding of random forests is that each tree will be grown until a certain minimum number of data points fit into each leaf of the tree. In other words, a tree will keep splitting until a certain level of classification of each data

问题 Other random forest tools have the "dial" that limits max depth of splits on a particular branch. h2o.randomForest has "max_depth", for example. What is the version of that for "ranger"? 回答1: I'm not familiar with the h2o.randomForest package, but my general understanding of random forests is that each tree will be grown until a certain minimum number of data points fit into each leaf of the tree. In other words, a tree will keep splitting until a certain level of classification of each data

问题 If understand correctly, when Random Forest estimators are calculated usually bootstrapping is applied, which means that a tree(i) is built only using data from sample(i), chosen with replacement. I want to know what is the size of the sample that sklearn RandomForestRegressor uses. The only thing that I see that is close: bootstrap : boolean, optional (default=True) Whether bootstrap samples are used when building trees. But there is no way to specify the size or proportion of the sample

问题 If understand correctly, when Random Forest estimators are calculated usually bootstrapping is applied, which means that a tree(i) is built only using data from sample(i), chosen with replacement. I want to know what is the size of the sample that sklearn RandomForestRegressor uses. The only thing that I see that is close: bootstrap : boolean, optional (default=True) Whether bootstrap samples are used when building trees. But there is no way to specify the size or proportion of the sample