Minimum number of observation when performing Random Forest
2013年7月9日 · Random forests produce reasonable results with low OOB errors (10-25%). Is there any rule of thumb regarding the minimum number of observations to use? In fact one of the response variable is unbalanced, and if I'm going to subsample it I will end up with an even smaller number of observations.
Plot trees for a Random Forest in Python with Scikit-Learn
2016年10月20日 · Assuming your Random Forest model is already fitted, first you should first import the export_graphviz function: from sklearn.tree import export_graphviz In your for cycle you could do the following to generate the dot file
random forest - Do I need to normalize (or scale) data for …
2012年1月22日 · Random Forest is a tree-based model and hence does not require feature scaling. This algorithm requires partitioning, even if you apply Normalization then also> the result would be the same. Share
machine learning - Random Forest Regression - How do I analyse …
2018年6月11日 · You can also add these two more metrics: from sklearn.metrics import accuracy_score, confusion_matrix accuracy_score(my_class_column, my_forest_train_prediction) confusion_matrix(my_test_data, my_prediction_test_forest)
Coefficient of the features in the decision function. random forest
2019年5月27日 · Random forest is an ensemble of decision trees, it is not a linear model. Sklearn provides importance of individual features which were used to train a random forest classifier or regressor. It can be accessed as follows, and returns an array of decimals which sum to 1. model.feature_importances_
random forest tuning - tree depth and number of trees
2016年1月25日 · Regarding the tree depth, standard random forest algorithm grow the full decision tree without pruning. A single decision tree do need pruning in order to overcome over-fitting issue. However, in random forest, this issue is eliminated by random selecting the variables and the OOB action.
r - How to improve randomForest performance? - Stack Overflow
2014年4月15日 · In Breiman's package, you can't directly set maxdepth, but use nodesize as a proxy for that, and also read all the good advice at: CrossValidated: "Practical questions on tuning Random Forests" So here your data has 4.2e+5 rows, then if each node shouldn't be smaller than ~0.1%, try nodesize=42 .
python - Sklearn random forest model is too big - Stack Overflow
2019年3月17日 · I have RandomForestClassifier model trained with the following parameters: n_estimators = 32, criterion = 'gini', max_depth = 380, This parameters are not randomly chosen, for some reason they showed the best performance...though seem strange to myself. The model size is about 5.5GB when saved with joblib.dump and compress=3. The data used is:
Choosing random_state for sklearn algorithms - Stack Overflow
2014年9月29日 · random state has a meaning beyond its application in sklearn (for example it is also used in Random Forest method). if you have a dataset like [1,2,3,4,5], arrangement of its elements can be randomized up to 5! orders (factorial of the length) which in this example is 120. so for example random_state = 0 is something like [2,3,5,4,1 ...
r - Save a random Forest object - Stack Overflow
Once a random forest model is created save the model as a file so that you can reuse it later. #model model_rf <- randomForest(y~ ., data = dataset, mtry=7,ntree=500) #write model saveRDS(model_rf , "model_rf.RDS") #load model readRDS("model_rf.RDS")