imports85

本程序是基于linux系统下c++代码

imports85            package:randomForest            R Documentation

_T_h_e _A_u_t_o_m_o_b_i_l_e _D_a_t_a

_D_e_s_c_r_i_p_t_i_o_n:

     This is the `Automobile' data from the UCI Machine Learning
     Repository.

_U_s_a_g_e:

     data(imports85)

_F_o_r_m_a_t:

     'imports85' is a data frame with 205 cases (rows) and 26 variables
     (columns).  This data set consists of three types of entities: (a)
     the specification of an auto in terms of various characteristics,
     (b) its assigned insurance risk rating, (c) its normalized losses
     in use as compared to other cars.  The second rating corresponds
     to the degree to which the auto is more risky than its price
     indicates.  Cars are initially assigned a risk factor symbol
     associated with its price.   Then, if it is more risky (or less),
     this symbol is adjusted by moving it up (or down) the scale. 
     Actuarians call this process `symboling'.  A value of +3 indicates
     that the auto is risky, -3 that it is probably pretty safe.

     The third factor is the relative average loss payment per insured
     vehicle year.  This value is normalized for all autos within a
     particular size classification (two-door small, station wagons,
     sports/speciality, etc...), and represents the average loss per
     car per year.

_A_u_t_h_o_r(_s):

     Andy Liaw

_S_o_u_r_c_e:

     Originally created by Jeffrey C. Schlimmer, from 1985 Model Import
     Car and Truck Specifications, 1985 Ward's Automotive Yearbook,
     Personal Auto Manuals, Insurance Services Office, and Insurance
     Collision Report, Insurance Institute for Highway Safety.

     The original data is at <URL:
     http://www.ics.uci.edu/~mlearn/MLSummary.html>.

_R_e_f_e_r_e_n_c_e_s:

     1985 Model Import Car and Truck Specifications, 1985 Ward's
     Automotive Yearbook.

     Personal Auto Manuals, Insurance Services Office, 160 Water
     Street, New York, NY 10038 

     Insurance Collision Report, Insurance Institute for Highway
     Safety, Watergate 600, Washington, DC 20037

_S_e_e _A_l_s_o:

     'randomForest'

_E_x_a_m_p_l_e_s:

     data(imports85)
     imp85 <- imports85[,-2]  # Too many NAs in normalizedLosses.
     imp85 <- imp85[complete.cases(imp85), ]
     ## Drop empty levels for factors.
     imp85[] <- lapply(imp85, function(x) if (is.factor(x)) x[, drop=TRUE] else x)

     stopifnot(require(randomForest))
     price.rf <- randomForest(price ~ ., imp85, do.trace=10, ntree=100)
     print(price.rf)
     numDoors.rf <- randomForest(numOfDoors ~ ., imp85, do.trace=10, ntree=100)
     print(numDoors.rf)