random-forest

问题 I'm trying to extract the feature importances of a random forest object I have trained using PySpark. However, I do not see an example of doing this anywhere in the documentation, nor is it a method of RandomForestModel. How can I extract feature importances from a RandomForestModel regressor or classifier in PySpark? Here's the sample code provided in the documentation to get us started; however, there is no mention of feature importances in it. from pyspark.mllib.tree import RandomForest

问题 I want to evaluate a random forest being trained on some data. Is there any utility in Apache Spark to do the same or do I have to perform cross validation manually? 回答1: ML provides CrossValidator class which can be used to perform cross-validation and parameter search. Assuming your data is already preprocessed you can add cross-validation as follows: import org.apache.spark.ml.Pipeline import org.apache.spark.ml.tuning.{ParamGridBuilder, CrossValidator} import org.apache.spark.ml

问题 Do you guys know where can I find examples of multiclass classification in Spark. I spent a lot of time searching in books and in the web, and so far I just know that it is possible since the latest version according the documentation. 回答1: ML ( Recommended in Spark 2.0+ ) We'll use the same data as in the MLlib below. There are two basic options. If Estimator supports multilclass classification out-of-the-box (for example random forest) you can use it directly: val trainRawDf = trainRaw.toDF

问题 I am using random forests in a big data problem, which has a very unbalanced response class, so I read the documentation and I found the following parameters: strata sampsize The documentation for these parameters is sparse (or I didn´t have the luck to find it) and I really don´t understand how to implement it. I am using the following code: randomForest(x=predictors, y=response, data=train.data, mtry=lista.params[1], ntree=lista.params[2], na.action=na.omit, nodesize=lista.params[3],

问题 I have a classification task with a time-series as the data input, where each attribute (n=23) represents a specific point in time. Besides the absolute classification result I would like to find out, which attributes/dates contribute to the result to what extent. Therefore I am just using the feature_importances_ , which works well for me. However, I would like to know how they are getting calculated and which measure/algorithm is used. Unfortunately I could not find any documentation on

问题 This question was migrated from Cross Validated because it can be answered on Stack Overflow. Migrated 6 years ago . I would like to run an unbiased cforest using the caret package. Is this possible? tc <- trainControl(method="cv", number=f, index=indexList, savePredictions=T, classProbs = TRUE, summaryFunction = twoClassSummary) createCfGrid <- function(len, data) { g = createGrid("cforest", len, data) g = expand.grid(.controls = cforest_unbiased(mtry = 5, ntree = 1000)) return(g) } set.seed

问题 In sci-kit learn, it's possible to access the entire tree structure, that is, each node of the tree. This allows to explore the attributes used at each split of the tree and which values are used for the test The binary tree structure has 5 nodes and has the following tree structure: node=0 test node: go to node 1 if X[:, 3] <= 0.800000011920929 else to node 2. node=1 leaf node. node=2 test node: go to node 3 if X[:, 2] <= 4.950000047683716 else to node 4. node=3 leaf node. node=4 leaf node.

问题 I tried to understand the 5 fold cross validation algorithm in Caret package but I could not find out how to get train set and test set for each fold and I also could not find this from the similar suggested questions. Imagine if I want to do cross validation by random forest method, I do the following: set.seed(12) train_control <- trainControl(method="cv", number=5,savePredictions = TRUE) rfmodel <- train(Species~., data=iris, trControl=train_control, method="rf") first_holdout <- subset

问题 i am getting this error.please give me any suggestion to resolve it.here is my code.i am taking traing data from train.csv and testing data from another file test.csv.i am new to machine learning so i could not understand what is the problem.give me any suggestion. import quandl,math import numpy as np import pandas as pd import matplotlib.pyplot as plt from matplotlib import style import datetime from sklearn.ensemble import RandomForestClassifier from sklearn.preprocessing import

问题 I have a dataset with 4669 observations and 15 variables. I am using Random forest to predict if a particular product will be accepted or not. With my latest data , I have my output variable with "Yes", "NO" and "". I wanted to predict if this "" will have Yes or No. I am using the following code. library(randomForest) outputvar <- c("Yes", "NO", "Yes", "NO", "" , "" ) inputvar1 <- c("M", "M", "F", "F", "M", "F") inputvar2 <- c("34", "35", "45", "60", "34", "23") data <- data.frame(cbind