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ucalyptus / skearn-random-forest-example.ipynb
Created June 10, 2020 06:30
skearn-random-forest-example.ipynb
View skearn-random-forest-example.ipynb
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View dataset.csv
card reports age income share expenditure owner selfemp dependents months majorcards active
yes 0 37.66667 4.52 0.03326991 124.9833 yes no 3 54 1 12
yes 0 33.25 2.42 0.0052169420000000005 9.854167 no no 3 34 1 13
yes 0 33.66667 4.5 0.0041555559999999995 15.0 yes no 4 58 1 5
yes 0 30.5 2.54 0.06521378 137.8692 no no 0 25 1 7
yes 0 32.16667 9.7867 0.06705059 546.5033 yes no 2 64 1 5
yes 0 23.25 2.5 0.0444384 91.99667 no no 0 54 1 1
yes 0 27.91667 3.96 0.01257576 40.83333 no no 2 7 1 5
yes 0 29.16667 2.37 0.07643376 150.79 yes no 0 77 1 3
yes 0 37.0 3.8 0.2456279 777.8217 yes no 0 97 1 6
View __init__.py
from .lsun import LSUN, LSUNClass
from .folder import ImageFolder, DatasetFolder
from .coco import CocoCaptions, CocoDetection
from .cifar import CIFAR10, CIFAR100
from .stl10 import STL10
from .mnist import MNIST, EMNIST, FashionMNIST, KMNIST, QMNIST
from .svhn import SVHN
from .phototour import PhotoTour
from .fakedata import FakeData
from .semeion import SEMEION
View application.csv
reports income active expenditure
0 4.52 12 124.9833
0 2.42 13 9.854167
0 4.5 5 15
0 2.54 7 137.8692
0 9.7867 5 546.5033
0 2.5 1 91.99667
0 3.96 5 40.83333
0 2.37 3 150.79
0 3.8 6 777.8217
View dataset.csv
card reports age income share expenditure owner selfemp dependents months majorcards active
yes 0 37.66667 4.52 0.03326991 124.9833 yes no 3 54 1 12
yes 0 33.25 2.42 0.0052169420000000005 9.854167 no no 3 34 1 13
yes 0 33.66667 4.5 0.0041555559999999995 15.0 yes no 4 58 1 5
yes 0 30.5 2.54 0.06521378 137.8692 no no 0 25 1 7
yes 0 32.16667 9.7867 0.06705059 546.5033 yes no 2 64 1 5
yes 0 23.25 2.5 0.0444384 91.99667 no no 0 54 1 1
yes 0 27.91667 3.96 0.01257576 40.83333 no no 2 7 1 5
yes 0 29.16667 2.37 0.07643376 150.79 yes no 0 77 1 3
yes 0 37.0 3.8 0.2456279 777.8217 yes no 0 97 1 6
View hetconv.py
import argparse
import os
import time
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
import torch.utils.data
import torchvision.transforms as transforms