decision-tree

问题 I am doing some machine learning in OpenCV and i'm using Decision Trees . I am currently using OpenCV 3.0.0-rc1 . Whenever i attempt to train Decision Trees with my training data and labels, i get either terminate called after throwing an instance of 'std::bad_alloc' what(): std::bad_alloc or Segmentation fault Depending on what i put into setMaxDepth(); if the number is larger than 22, it's bad_alloc, else it's seg fault. Here's my source code: //import data Mat trainData=imread("/home

问题 I am using R to classify a data-frame called 'd' containing data structured like below: The data has 576666 rows and the column "classLabel" has a factor of 3 levels: ONE, TWO, THREE. I am making a decision tree using rpart: fitTree = rpart(d$classLabel ~ d$tripduration + d$from_station_id + d$gender + d$birthday) And I want to predict the values for the "classLabel" for newdata : newdata = data.frame( tripduration=c(345,244,543,311), from_station_id=c(60,28,100,56), gender=c("Male","Female",

问题 I need to extract information from the rules in decision tree. I am using rpart package in R. I am using demo data in the package to explain my requirements: data(stagec) fit<- rpart(formula = pgstat ~ age + eet + g2 + grade + gleason + ploidy, data = stagec, method = "class", control=rpart.control(cp=0.05)) fit printing fit shows n= 146 node), split, n, loss, yval, (yprob) * denotes terminal node 1) root 146 54 0 (0.6301370 0.3698630) 2) grade< 2.5 61 9 0 (0.8524590 0.1475410) * 3) grade>=2

问题 I need to extract information from the rules in decision tree. I am using rpart package in R. I am using demo data in the package to explain my requirements: data(stagec) fit<- rpart(formula = pgstat ~ age + eet + g2 + grade + gleason + ploidy, data = stagec, method = "class", control=rpart.control(cp=0.05)) fit printing fit shows n= 146 node), split, n, loss, yval, (yprob) * denotes terminal node 1) root 146 54 0 (0.6301370 0.3698630) 2) grade< 2.5 61 9 0 (0.8524590 0.1475410) * 3) grade>=2

问题 I am applying Decision Tree to a data set, using sklearn In Sklearn there is a parameter to select the depth of the tree - dtree = DecisionTreeClassifier(max_depth=10). My question is how the max_depth parameter helps on the model. how does high/low max_depth help in predicting the test data more accurately? 回答1: max_depth is what the name suggests: The maximum depth that you allow the tree to grow to. The deeper you allow, the more complex your model will become. For training error, it is

问题 I used the following code to get the decision tree of CHAID independent_variable_columns = ['gender', 'grade', 'no_renewals', 'complaint_count'] dep_variable = 'switch' tree = Tree.from_pandas_df( df, dict(zip(independent_variable_columns, ['nominal'] * 38)), dep_variable, max_depth=2 ) tree.to_tree() tree.render() but I am getting the following error File "C:\Users\data\AppData\Local\Continuum\anaconda3\lib\site-packages\CHAID\graph.py", line 93, in <listcomp> [os.remove(file) for file in

问题 I used the following code to get the decision tree of CHAID independent_variable_columns = ['gender', 'grade', 'no_renewals', 'complaint_count'] dep_variable = 'switch' tree = Tree.from_pandas_df( df, dict(zip(independent_variable_columns, ['nominal'] * 38)), dep_variable, max_depth=2 ) tree.to_tree() tree.render() but I am getting the following error File "C:\Users\data\AppData\Local\Continuum\anaconda3\lib\site-packages\CHAID\graph.py", line 93, in <listcomp> [os.remove(file) for file in

问题 I tried to run this but I am getting GraphViz's executables not found. I have done pip install graphviz but no success import pandas from sklearn import tree import pydotplus from sklearn.tree import DecisionTreeClassifier import matplotlib.pyplot as plt import matplotlib.image as pltimg df = pandas.read_csv("shows.csv") d = {'UK': 0, 'USA': 1, 'N': 2} df['Nationality'] = df['Nationality'].map(d) d = {'YES': 1, 'NO': 0} df['Go'] = df['Go'].map(d) features = ['Age', 'Experience', 'Rank',

问题 I tried to run this but I am getting GraphViz's executables not found. I have done pip install graphviz but no success import pandas from sklearn import tree import pydotplus from sklearn.tree import DecisionTreeClassifier import matplotlib.pyplot as plt import matplotlib.image as pltimg df = pandas.read_csv("shows.csv") d = {'UK': 0, 'USA': 1, 'N': 2} df['Nationality'] = df['Nationality'].map(d) d = {'YES': 1, 'NO': 0} df['Go'] = df['Go'].map(d) features = ['Age', 'Experience', 'Rank',

问题 I would like to be able to personalize the plot that it is displayed to include standard deviation and statistical significance of the regressors after using the partykit::mob() function. The following code is from partykit documentation. library("partykit") if(require("mlbench")) { ## Pima Indians diabetes data data("PimaIndiansDiabetes", package = "mlbench") ## a simple basic fitting function (of type 1) for a logistic regression logit <- function(y, x, start = NULL, weights = NULL, offset